Molecular Property Prediction

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Overview

Molecular property prediction studies how to infer quantities such as solubility, toxicity, binding affinity, and quantum energies from molecules, mixtures, or molecular environments. Common approaches use fingerprints and physicochemical descriptors, graph neural networks, molecular language models, and 3D equivariant or multimodal models; some also encode physical laws or learn from experimental and simulated data together. Models are assessed on benchmarks such as MoleculeNet and TDC, with scaffold, temporal, and other out-of-distribution splits used to test transfer to unfamiliar chemistry. A central challenge is that strong in-distribution scores and larger pretrained models do not guarantee reliable extrapolation, while careful classical baselines, data quality, uncertainty estimates, and evaluation design can strongly affect results.

Latest papers

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204 papers29 papers in the last four weeks, level with the four weeks before. 0.2% of all new papers.

Mar 12, 2026

Can Decision Trees Teach Large Language Models? Distilling Verbalized Knowledge for Molecular Property Prediction

Molecular Property Prediction (MPP) is a fundamental problem in drug discovery that has recently attracted growing attention. Large Language Models (LLMs), known for their impressive proficiency across domains, show promise as generalist models for MPP. However, their current performance remains below the threshold needed for practical adoption. To bridge this gap, we propose TreeKD for distilling the knowledge of tree-based specialist models into LLMs to complement the internal knowledge of LLMs and improve their predictive accuracy. For each property, we train a specialist decision tree using features derived from 40K functional groups in the input molecules. Then, the predictive rule learned by the decision tree, which encodes its knowledge, is verbalized and incorporated into the prompts for training LLMs. In addition, by replacing a single decision tree with a Random Forest, we introduce a test-time scaling technique called rule-consistency, which aggregates predictions generated from different prompts constructed with different rules. An extensive evaluation with two LLMs, Gemma-2-2B and Granite-3.3-2B, on the TDC benchmark with 22 prediction tasks shows that our method substantially enhances the performance of LLMs, advancing the development of generalist models for MPP.

Khiem Le, Sreejata Dey, Marcos Martínez Galindo +4

University of Notre Dame, IN, USA · IBM Research

Molecular Property PredictionKnowledge DistillationLLM Prompting
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Mar 9, 2026

Hybrid Quantum-Classical Encoding for Accurate Residue-Level pKa Prediction

Accurate prediction of residue-level pKa values is essential for understanding protein function, stability, and reactivity. While existing resources such as DeepKaDB and CpHMD-derived datasets provide valuable training data, their descriptors remain primarily classical and often struggle to generalize across diverse biochemical environments. We introduce a reproducible hybrid quantum-classical framework that enriches residue-level representations with a Gaussian kernel-based quantum-inspired feature mapping. These quantum-enhanced descriptors are combined with normalized structural features to form a unified hybrid encoding processed by a Deep Quantum Neural Network (DQNN). This architecture captures nonlinear relationships in residue microenvironments that are not accessible to classical models. Benchmarking across multiple curated descriptor sets demonstrates that the DQNN achieves improved cross-context generalization relative to classical baselines. External evaluation on the PKAD-R experimental benchmark and an Aββ40 case study further highlights the robustness and transferability of the quantum-inspired representation. By integrating quantum-inspired feature transformations with classical biochemical descriptors, this work establishes a scalable and experimentally transferable approach for residue-level pKa prediction and broader applications in protein electrostatics.

Van Le, Tan Le

the Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA · the School of Engineering, Architecture and Aviation, Hampton University, Hampton, VA 23669, USA

Molecular Property PredictionProtein Representation LearningQuantum-Inspired MLHybrid Quantum-Classical ML
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Mar 3, 2026

MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery

General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks. Simply increasing model size or introducing reasoning tokens does not yield significant performance gains. To address this gap, we introduce the MMAI Gym for Science, a one-stop shop molecular data formats and modalities as well as task-specific reasoning, training, and benchmarking recipes designed to teach foundation models the 'language of molecules' in order to solve practical drug discovery problems. We use MMAI Gym to train an efficient Liquid Foundation Model (LFM) for these applications, demonstrating that smaller, purpose-trained foundation models can outperform substantially larger general-purpose or specialist models on molecular benchmarks. Across essential drug discovery tasks - including molecular optimization, ADMET property prediction, retrosynthesis, drug-target activity prediction, and functional group reasoning - the resulting model achieves near specialist-level performance and, in the majority of settings, surpasses larger models, while remaining more efficient and broadly applicable in the domain.

Maksim Kuznetsov, Zulfat Miftahutdinov, Rim Shayakhmetov +17

Insilico Medicine · Liquid AI

Molecular OptimizationMolecular Property PredictionMolecular Foundation ModelsRetrosynthesisMolecular Representation LearningDrug Discovery
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Mar 3, 2026

Information Routing in Atomistic Foundation Models: How Task Alignment and Equivariance Shape Linear Disentanglement

What determines whether a molecular property prediction model organizes its representations so that geometric and compositional information can be cleanly separated? We introduce Compositional Probe Decomposition (CPD), which linearly projects out composition signal and measures how much geometric information remains accessible to a Ridge probe. We validate CPD with four independent checks, including a structural isomer benchmark where compositional projections score at chance while geometric residuals reach 94.6% pairwise classification accuracy. Across ten models from five architectural families on QM9, we find a linear accessibility gradient: models differ by 6.6×6.6\times in geometric information accessible after composition removal (Rgeom2R^2_{\mathrm{geom}} from 0.081 to 0.533 for HOMO-LUMO gap). Three factors explain this gradient. Task alignment dominates: models trained on HOMO-LUMO gap (Rgeom2R^2_{\mathrm{geom}} 0.44--0.53) outscore energy-trained models by ∼\sim0.25 R2R^2 regardless of architecture. Within-architecture ablations on two independent architectures confirm this: PaiNN drops from 0.53 to 0.31 when retrained on energy, and MACE drops from 0.44 to 0.08. Data diversity partially compensates for misaligned objectives, with MACE pretrained on MPTraj (0.36) outperforming QM9-only energy models. Inside MACE's representations, information routes by symmetry type: L=1L{=}1 (vector) channels preferentially encode dipole moment (R2=0.59R^2 = 0.59 vs.\ 0.38 in L=0L{=}0), while L=0L{=}0 (scalar) channels encode HOMO-LUMO gap (R2=0.76R^2 = 0.76 vs.\ 0.34 in L=1L{=}1). This pattern is absent in ViSNet. We also show that nonlinear probes produce misleading results on residualized representations, recovering R2=0.68R^2 = 0.68--0.950.95 on a purely compositional target, and recommend linear probes for this setting.

Joshua Steier

Independent Researcher

Molecular Representation LearningRepresentation DisentanglementMolecular Foundation ModelsMolecular Property PredictionEquivariant Neural NetworksLinear Probing
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Mar 3, 2026

ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures

Accurate prediction of the physicochemical properties of molecular mixtures using graph neural networks remains a significant challenge, as it requires simultaneous embedding of intramolecular interactions while accounting for mixture composition (i.e., concentrations and ratios). Existing approaches are ill-equipped to emulate realistic mixture environments, where densely coupled interactions propagate across hierarchical levels - from atoms and functional groups to entire molecules - and where cross-level information exchange is continuously modulated by composition. To bridge the gap between isolated molecules and realistic chemical environments, we present ChemFlow, a novel hierarchical framework that integrates atomic, functional group, and molecular-level features, facilitating information flow across these levels to predict the behavior of complex chemical mixtures. ChemFlow employs an atomic-level feature fusion module, Chem-embed, to generate context-aware atomic representations influenced by the mixture state and atomic characteristics. Next, bidirectional group-to-molecule and molecule-to-group attention mechanisms enable ChemFlow to capture functional group interactions both within and across molecules in the mixture. By dynamically adjusting representations based on concentration and composition, ChemFlow excels at predicting concentration-dependent properties and significantly outperforms state-of-the-art models in both concentration-sensitive and concentration-independent systems. Extensive experiments demonstrate ChemFlow's superior accuracy and efficiency in modeling complex chemical mixtures.

Jinming Fan, Chao Qian, Wilhelm T. S. Huck +2

College of Chemical and Biological Engineering, Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, Zhejiang University, 310027 Hangzhou (P. R. China) · Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands · Institute of Zhejiang University – Quzhou, 324000 Quzhou (P.R. China)

Molecular Property PredictionMolecular Representation LearningHierarchical Representation LearningMaterials Science
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Mar 1, 2026

GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models

Machine learning accelerates molecular property prediction, yet state-of-the-art Large Language Models and Graph Neural Networks operate as black boxes. In drug discovery, where safety is critical, this opacity risks masking false correlations and excluding human expertise. Existing interpretability methods suffer from the effectiveness-trustworthiness trade-off: explanations may fail to reflect a model's true reasoning, degrade performance, or lack domain grounding. Concept Bottleneck Models (CBMs) offer a solution by projecting inputs to human-interpretable concepts before readout, ensuring that explanations are inherently faithful to the decision process. However, adapting CBMs to chemistry faces three challenges: the Relevance Gap (selecting task-relevant concepts from a large descriptor space), the Annotation Gap (obtaining concept supervision for molecular data), and the Capacity Gap (degrading performance due to bottleneck constraints). We introduce GlassMol, a model-agnostic CBM that addresses these gaps through automated concept curation and LLM-guided concept selection. Experiments across thirteen benchmarks demonstrate that \method generally matches or exceeds black-box baselines, suggesting that interpretability does not sacrifice performance and challenging the commonly assumed trade-off. Code is available at https://github.com/walleio/GlassMol.

Oscar Rivera, Ziqing Wang, Matthieu Dagommer +2

Northwestern University, Evanston, IL 60201, USA · AbbVie, Chicago, IL 60064, USA

Interpretable MLMolecular Property PredictionConcept Bottleneck Models
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Mar 1, 2026

MultiPUFFIN: A Multimodal Domain-Constrained Foundation Model for Molecular Property Prediction of Small Molecules

MultiPUFFIN is a domain-informed multimodal foundation model for predicting thermophysical properties of small molecules, addressing a critical gap in chemical engineering, drug discovery, and materials science. Existing molecular foundation models pretrain on millions of molecules to learn general-purpose representations, but their standard MLP output layers impose no physical constraints, vapor pressure predictions may violate monotonic temperature dependence, and viscosity curves may lack the functional form required by process simulators. Domain-informed approaches that guarantee thermodynamic consistency have remained limited to single properties and small datasets, whereas multimodal foundation models have focused on biological activity rather than thermophysical properties. MultiPUFFIN fills this gap by fusing SMILES sequences, 2D molecular graphs, and 3D conformer geometries through bidirectional cross-modal attention and gated fusion, supplemented by auxiliary encoders for experimental conditions and molecular descriptors. The backbone is pretrained on 500,000 unlabelled PubChem molecules using three complementary self-supervised objectives. A condition-aware refinement stack of five conditioners (temperature, pH, pressure, polymorph, and measurement method) routes each property to a four-head tournament that selects the best-performing thermodynamically informed head for that property. MultiPUFFIN achieves a mean test R2 of 0.784 and outperforms fine-tuned ChemBERTa-2 on all nine properties despite training on roughly 2,000x fewer labeled molecules.

Idelfonso B. R. Nogueira, Carine M. Rebello, Mumin Enis Leblebici +1

Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, 7034, Norway · Faculty of Industrial Engineering, KU Leuven, Diepenbeek, Belgium · University of Surrey, Guildford, United Kingdom

Molecular Property PredictionMultimodal Foundation ModelsMolecular Representation LearningScientific MLMultimodal Pretraining
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Feb 28, 2026

Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions

Molecule representation learning is crucial for understanding and predicting molecular properties. However, conventional atom-centric models, which treat chemical bonds merely as pairwise interactions, often overlook complex bond-level phenomena like resonance and stereoselectivity. This oversight limits their predictive accuracy for nuanced chemical behaviors. To address this limitation, we introduce DeMol, a dual-graph framework whose architecture is motivated by a rigorous information-theoretic analysis demonstrating the information gain from a bond-centric perspective. DeMol explicitly models molecules through parallel atom-centric and bond-centric channels. These are synergistically fused by multi-scale Double-Helix Blocks designed to learn intricate atom-atom, atom-bond, and bond-bond interactions. The framework's geometric consistency is further enhanced by a regularization term based on covalent radii to enforce chemically plausible structures. Comprehensive evaluations on diverse benchmarks, including PCQM4Mv2, OC20 IS2RE, QM9, and MoleculeNet, show that DeMol establishes a new state-of-the-art, outperforming existing methods. These results confirm the superiority of explicitly modelling bond information and interactions, paving the way for more robust and accurate molecular machine learning.

Yunqing Liu, Yi Zhou, Wenqi Fan

Department of Computing (COMP), The Hong Kong Polytechnic University · Department of Management and Marketing (MM), The Hong Kong Polytechnic University

Molecular Property PredictionMolecular Representation LearningGraph Representation Learning
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Feb 27, 2026

Tensor Hypercontraction Error Correction Using Regression

Wavefunction-based quantum methods are some of the most accurate tools for predicting and analyzing the electronic structure of molecules, in particular for accounting for dynamical electron correlation. However, most methods of including dynamical correlation beyond the simple second-order Møller-Plesset perturbation theory (MP2) level are too computationally expensive to apply to large molecules. Approximations which reduce scaling with system size are a potential remedy, such as the tensor hyper-contraction (THC) technique of Hohenstein et al., but also result in additional sources of error. In this work, we correct errors in THC-approximated methods using machine learning. Specifically, we apply THC to third-order Møller-Plesset theory (MP3) as a simplified model for coupled cluster with single and double excitations (CCSD), and train several regression models on observed THC errors from the Main Group Chemistry Database (MGCDB84). We compare performance of multiple linear regression models and non-linear Kernel Ridge regression models. We also investigate correlation procedures using absolute and relative corrections and evaluate the corrections for both molecule and reaction energies. We discuss the potential for using regression techniques to correct THC-MP3 errors by comparing it to the "canonical" MP3 reference values and find the optimum technique based on accuracy. We find that non-linear regression models reduced root mean squared errors between THC- and canonical MP3 by a factor of 6-9×\times for total molecular energies and 2-3×\times for reaction energies.

Ishna Satyarth, Eric C. Larson, Devin A. Matthews

Department of Computer Science, Southern Methodist University, Dallas, TX 75275, USA · Department of Chemistry, Southern Methodist University, Dallas, TX 75275, USA

Computational Quantum ChemistryQuantum ChemistryMolecular Property Prediction
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Feb 26, 2026

Prediction of Diffusion Coefficients in Mixtures with Tensor Completion

Predicting diffusion coefficients in mixtures is crucial for many applications, as experimental data remain scarce, and machine learning (ML) offers promising alternatives to established semi-empirical models. Among ML models, matrix completion methods (MCMs) have proven effective in predicting thermophysical properties, including diffusion coefficients in binary mixtures. However, MCMs are restricted to single-temperature predictions, and their accuracy depends strongly on the availability of high-quality experimental data for each temperature of interest. In this work, we address this challenge by presenting a hybrid tensor completion method (TCM) for predicting temperature-dependent diffusion coefficients at infinite dilution in binary mixtures. The TCM employs a Tucker decomposition and is jointly trained on experimental data for diffusion coefficients at infinite dilution in binary systems at 298 K, 313 K, and 333 K. Predictions from the semi-empirical SEGWE model serve as prior knowledge within a Bayesian training framework. The TCM then extrapolates linearly to any temperature between 268 K and 378 K, achieving markedly improved prediction accuracy compared to established models across all studied temperatures. To further enhance predictive performance, the experimental database was expanded using active learning (AL) strategies for targeted acquisition of new diffusion data by pulsed-field gradient (PFG) NMR measurements. Diffusion coefficients at infinite dilution in 19 solute + solvent systems were measured at 298 K, 313 K, and 333 K. Incorporating these results yields a substantial improvement in the TCM's predictive accuracy. These findings highlight the potential of combining data-efficient ML methods with adaptive experimentation to advance predictive modeling of transport properties.

Zeno Romero, Kerstin Münnemann, Hans Hasse +1

Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern, Erwin-Schrödinger-Str. 44, 67663 Kaiserslautern, Germany

Molecular Property PredictionTensor Completion
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Feb 25, 2026

MolFM-Lite: Multi-Modal Molecular Property Prediction with Conformer Ensemble Attention and Cross-Modal Fusion

Most machine learning models for molecular property prediction rely on a single molecular representation (either a sequence, a graph, or a 3D structure) and treat molecular geometry as static. We present MolFM-Lite, a multi-modal model that jointly encodes SELFIES sequences (1D), molecular graphs (2D), and conformer ensembles (3D) through cross-attention fusion, while conditioning predictions on experimental context via Feature-wise Linear Modulation (FiLM). Our main methodological contributions are: (1) a conformer ensemble attention mechanism that combines learnable attention with Boltzmann-weighted priors over multiple RDKit-generated conformers, capturing the thermodynamic distribution of molecular shapes; and (2) a cross-modal fusion layer where each modality can attend to others, enabling complementary information sharing. We evaluate on four MoleculeNet scaffold-split benchmarks using our model's own splits, and report all baselines re-evaluated under the same protocol. Comprehensive ablation studies across all four datasets confirm that each architectural component contributes independently, with tri-modal fusion providing 7-11% AUC improvement over single-modality baselines and conformer ensembles adding approximately 2% over single-conformer variants. Pre-training on ZINC250K (~250K molecules) using cross-modal contrastive and masked-atom objectives enables effective weight initialization at modest compute cost. We release all code, trained models, and data splits to support reproducibility.

Syed Omer Shah, Mohammed Maqsood Ahmed, Danish Mohiuddin Mohammed +2

Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA · Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA · Department of Computer Science, Muffakham Jah College of Engineering and Technology, Hyderabad, India +1

Molecular Property PredictionMultimodal Fusion
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Feb 24, 2026

Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials

General-purpose 3D modeling in chemistry encompasses molecules and materials, requiring both generative and predictive capabilities. However, most existing AI approaches are optimized for a single domain (molecules or materials) and a single task (generation or prediction), which limits representation sharing and transfer. We introduce Zatom-1, a cross-domain, general-purpose model architecture that unifies generative and predictive learning of 3D molecules and materials. Zatom-1 is a deliberately simplified Transformer trained with a multimodal flow matching objective that jointly models discrete atom types and continuous 3D geometries. This approach supports scalable pretraining with predictable gains as model capacity increases, while enabling fast and stable sampling. We use cross-domain generative pretraining as a universal initialization for downstream multi-task prediction of properties, energies, and forces. Empirically, Zatom-1 outperforms or competes with specialized baselines on both multi-task generative and predictive benchmarks in data-controlled settings, while improving generative inference speed by more than an order of magnitude. Our experiments demonstrate positive predictive transfer between data domains from joint generative pretraining: modeling materials during generative pretraining improves molecular property prediction accuracy. Open-source code and model weights are freely available at https://github.com/Zatom-AI/zatom.

Alex Morehead, Miruna Cretu, Antonia Panescu +14

LBNL · ICSI · University of Cambridge +3

3D Foundation ModelsMultimodal Foundation ModelsMolecular GenerationMolecular Property PredictionMaterials Property Prediction
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Feb 24, 2026

MolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks

Molecules are often represented as SMILES strings, which can be readily converted to hand-crafted descriptors or fingerprints (FP) for molecular property prediction. Research has demonstrated that SMILES can be converted to molecular graphs G=(V,E)G = (V, E), with atoms as nodes (V)(V) and bonds as edges (E)(E). These molecular graphs can subsequently be used to train graph neural networks (GNN) models. Despite the recent surge in application of GNN (existing and novel architectures) for molecular property prediction, a rigorous benchmark is still lacking. We propose MolGraphBench, a comprehensive benchmark of four commonly used GNN models for molecular property prediction. Benchmarking results demonstrate graph convolutional network (GCN) and graph isomorphism networks (GIN) as the optimal GNN architectures for molecular graph regression tasks, based on absolute performance, training efficiency, transfer learning and prediction quality. The study also indicates the non-complementary nature of molecular fingerprints in the fusion (GNN-FP) framework. Furthermore, our GNN models achieved performance superior or comparable performance to current state-of-the-art GNN baselines across three datasets (GCN with RMSE of 0.5180.518 on B3DB, GIN-FP with RMSE of 1.0221.022 on FreeSolv and GIN with MAE of 63.78363.783 on RT datasets). Findings from this study indicate that type of GNN-layer, should be treated as a tunable hyperparameter rather than a fixed design choice to achieve superior performance.

Rajan, Ishaan Gupta

School of Interdisciplinary Research (SIRe), IIT Delhi, New Delhi-110016, India · Department of Biochemical Engineering and Biotechnology (DBEB), IIT Delhi, New Delhi-110016, India

Graph Neural NetworksMolecular Property PredictionMolecular Representation Learning
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Feb 24, 2026

Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference

Modern Protein Language Models (PLMs) apply transformer-based model architectures from natural language processing to biological sequences, predicting a variety of protein functions and properties. However, protein language has key differences from natural language, such as a rich functional space despite a vocabulary of only 20 amino acids. These differences motivate research into how transformer-based architectures operate differently in the protein domain and how we can better leverage PLMs to solve protein-related tasks. In this work, we begin by directly comparing how the distribution of information stored across layers of attention heads differs between the protein and natural language domain. Furthermore, we adapt a simple early-exit technique-originally used in the natural language domain to improve efficiency at the cost of performance-to achieve both increased accuracy and substantial efficiency gains in protein non-structural property prediction by allowing the model to automatically select protein representations from the intermediate layers of the PLMs for the specific task and protein at hand. We achieve performance gains ranging from 0.4 to 7.01 percentage points while simultaneously improving efficiency by over 10 percent across models and non-structural prediction tasks. Our work opens up an area of research directly comparing how language models change behavior when moved into the protein domain and advances language modeling in biological domains.

Anna Hart, Chi Han, Jeonghwan Kim +2

Siebel School of Data Science and Computing, University of Illinois Urbana-Champaign, 201 N Goodwin Ave, Urbana, IL 61801, USA · DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, 1206 W Gregory Drive, Urbana, IL 61801, USA · Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, 600 S Matthews Ave, Urbana, IL 61801, USA

Protein Language ModelsProtein Representation LearningBioinformaticsMolecular Property Prediction
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Feb 23, 2026

MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry

Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on local atomic descriptors that cannot capture long-range electrostatic effects. We present a new electrostatic foundation model for molecular chemistry that extends the MACE architecture with explicit treatment of long-range interactions and electrostatic induction. Our approach combines local many-body geometric features with a non-self-consistent field formalism that updates learnable charge and spin densities through polarisable iterations to model induction, followed by global charge equilibration via learnable Fukui functions to control total charge and total spin. This design enables an accurate and physical description of systems with varying charge and spin states while maintaining computational efficiency. Trained on the OMol25 dataset of 100 million hybrid DFT calculations, our models achieve chemical accuracy across diverse benchmarks, with accuracy competitive with hybrid DFT on thermochemistry, reaction barriers, conformational energies, and transition metal complexes. Notably, we demonstrate that the inclusion of long-range electrostatics leads to a large improvement in the description of non-covalent interactions and supramolecular complexes over non-electrostatic models, including sub-kcal/mol prediction of molecular crystal formation energy in the X23-DMC dataset and a fourfold improvement over short-ranged models on protein-ligand interactions. The model's ability to handle variable charge and spin states, respond to external fields, provide interpretable spin-resolved charge densities, and maintain accuracy from small molecules to protein-ligand complexes positions it as a versatile tool for computational molecular chemistry and drug discovery.

Ilyes Batatia, William J. Baldwin, Domantas Kuryla +10

Engineering Laboratory, University of Cambridge, Trumpington St, Cambridge, UK · Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK · Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge, UK +6

Molecular Foundation ModelsMachine Learning Interatomic PotentialsQuantum ChemistryMolecular Property PredictionMolecular Representation Learning
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Feb 22, 2026

KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction

Pro-inflammatory peptides (PIPs) play critical roles in immune signaling and inflammation but are difficult to identify experimentally due to costly and time-consuming assays. To address this challenge, we present KEMP-PIP, a hybrid machine learning framework that integrates deep protein embeddings with handcrafted descriptors for robust PIP prediction. Our approach combines contextual embeddings from pretrained ESM protein language models with multi-scale k-mer frequencies, physicochemical descriptors, and modlAMP sequence features. Feature pruning and class-weighted logistic regression manage high dimensionality and class imbalance, while ensemble averaging with an optimized decision threshold enhances the sensitivity--specificity balance. Through systematic ablation studies, we demonstrate that integrating complementary feature sets consistently improves predictive performance. On the standard benchmark dataset, KEMP-PIP achieves an MCC of 0.505, accuracy of 0.752, and AUC of 0.762, outperforming ProIn-fuse, MultiFeatVotPIP, and StackPIP. Relative to StackPIP, these results represent improvements of 9.5% in MCC and 4.8% in both accuracy and AUC. The KEMP-PIP web server is freely available at https://nilsparrow1920-kemp-pip.hf.space/ and the full implementation at https://github.com/S18-Niloy/KEMP-PIP.

Soumik Deb Niloy, Md. Fahmid-Ul-Alam Juboraj, Swakkhar Shatabda

Department of Computer Science and Engineering, BRAC University, Dhaka 1212, Bangladesh

Molecular Property PredictionFeature Fusion
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Feb 20, 2026

Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria

Machine learning (ML) approaches have shown promising results for predicting molecular properties relevant for chemical process design. However, they are often limited by scarce experimental property data and lack thermodynamic consistency. As such, thermodynamics-informed ML, i.e., incorporating thermodynamic relations into the loss function as regularization term for training, has been proposed. We herein transfer the concept of thermodynamics-informed graph neural networks (GNNs) from the Gibbs-Duhem to the Clapeyron equation, predicting several pure component properties in a multi-task manner, namely: vapor pressure, liquid molar volume, vapor molar volume and enthalpy of vaporization. We find improved prediction accuracy of the Clapeyron-GNN compared to the single-task learning setting, and improved approximation of the Clapeyron equation compared to the purely data-driven multi-task learning setting. In fact, we observe the largest improvement in prediction accuracy for the properties with the lowest availability of data, making our model promising for practical application in data scarce scenarios of chemical engineering practice.

Jan Pavšek, Alexander Mitsos, Elvis J. Sim +1

Process Systems Engineering (AVT.SVT), RWTH Aachen University · JARA Center for Simulation and Data Science (CSD) · Institute of Climate and Energy Systems ICE-1: Energy Systems Engineering, Forschungszentrum Jülich GmbH

Molecular Property PredictionMaterials Property PredictionPhysics-Informed MLPhysics-Informed GNNs
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Dec 26, 2025

DuaDeep-SeqAffinity: Dual-Branch Deep Learning for Tri-Stream Sequence-Based Antibody--Antigen Affinity Prediction

DuaDeep-SeqAffinity is a sequence-only deep learning framework that predicts antibody--antigen binding affinity directly from primary amino acid sequences, avoiding the cost and scarcity of resolved three-dimensional structures. The antigen and the antibody heavy and light chains are processed as three independent streams, each embedded with a frozen ESM-2 protein language model and passed through parallel Transformer and convolutional neural network (CNN) branches before late fusion, a decoupled design intended to preserve local complementarity-determining region (CDR) signal that monolithic encoders can dilute. On a sequence-disjoint split of the AbRank benchmark, the model achieves a Pearson correlation of 0.683, an R^2 of 0.460, and a pairwise ranking AUC of 0.895, significantly outperforming single-branch ablations (paired t-test, p < 0.05). Attention-map and gradient-based saliency analyses further show that the model preferentially attends to CDR loops and candidate epitope residues, supporting its use as a scalable, structure-free tool for high-throughput antibody screening.

Aicha Boutorh, Soumia Bouyahiaoui, Manel Kara Laouar +3

National School of Artificial Intelligence (ENSIA) Sidi Abdallah Campus, Algiers, Algeria · Lamine Debaghine University Hospital Center, CHU Bab El Oued, Algiers, Algeria

Hybrid CNN-Transformer ArchitecturesMolecular Property Prediction
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Nov 28, 2025

Freeze, Diffuse, Decode: Task-Aware Adaptation of Transformer Embeddings for Antimicrobial Peptide Design

Pretrained transformers provide rich, general-purpose embeddings, which are transferred to downstream tasks. However, current transfer strategies: fine-tuning and probing, either distort the pretrained geometric structure of the embeddings or lack sufficient expressivity to capture task-relevant signals. These issues become even more pronounced when supervised data are scarce. Here, we introduce Freeze, Diffuse, Decode (FDD), a novel diffusion-based framework that adapts pre-trained embeddings to downstream tasks while preserving their underlying geometric structure. FDD propagates supervised signal along the intrinsic manifold of frozen embeddings, enabling a geometry-aware adaptation of the embedding space. Applied to antimicrobial peptide design, FDD yields low-dimensional, predictive, and interpretable representations that support property prediction, retrieval, and latent-space interpolation.

Pankhil Gawade, Adam Izdebski, Myriam Lizotte +4

Institute of AI for Health, Helmholtz Munich · School of Computation, Information and Technology, Technical University of Munich · Mila - Quebec AI institute; Univ. de Montreal +3

Representation LearningGeometric Representation LearningMolecular Property Prediction
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Jul 4, 2025

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization

Explainable artificial intelligence approaches accelerate drug discovery by improving molecular representation learning, identifying key molecular structures, and rationalizing drug property prediction. However, developing end-to-end explainable models for target-specific structure-activity relationship modeling remains challenging because compound-protein interaction data are often limited for individual targets, and small changes in chemical substituents or local structural motifs can cause large differences in molecular properties. Therefore, effectively leveraging structural and property information to identify key moieties associated with compound-protein affinity is essential. We propose a graph neural network (GNN) framework that uses property and structural information from activity-cliff molecule pairs targeting specific proteins to predict compound-protein affinity, measured by half-maximal inhibitory concentration (IC50), and explain property differences. To improve explainability, we trained GNNs with structure-aware loss functions using group lasso and sparse group lasso regularization, which prune and highlight molecular subgraphs relevant to activity differences. We applied this framework to activity-cliff data from molecules targeting six tyrosine-protein kinases across the Src, Abl, and Tec families, as well as anaplastic lymphoma kinase. Integrating common- and uncommon-node information with sparse group lasso improved target-specific molecular property prediction, producing lower root mean square errors and higher Pearson correlation coefficients. Regularization also enhanced GNN feature attribution by improving graph-level global direction scores and atom-level coloring accuracy. These results support more interpretable drug discovery pipelines, particularly for identifying critical molecular substructures during lead optimization.

Zanyu Shi, Yang Wang, Pathum M. Weerawarna +4

Department of Biostatistics & Health Data Science, Indiana University Fairbanks School of Public Health, Indianapolis, 46202 IN, USA. · Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, 47405 IN, USA. · Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, 46202 IN, USA. +2

Protein-Ligand Binding Affinity PredictionMolecular Property PredictionGNN ExplainabilityDrug Discovery
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Jul 3, 2025

A Machine Learning Benchmarking Framework for Lipid Nanoparticle Transfection Efficiency Prediction

The discovery of new ionizable lipids for efficient lipid nanoparticle (LNP)-mediated RNA delivery remains a major bottleneck in RNA therapeutics development. Recent advances demonstrate the potential of machine learning (ML) models to predict transfection efficiency directly from lipid structure, enabling high-throughput virtual screening and accelerating lead identification. However, as new models for LNP transfection prediction continue to emerge, the lack of rigorous and standardized benchmarking poses a significant risk and may undermine confidence in their reliability for discovery. Here, we present a robust ML benchmarking framework for evaluating transfection prediction models based on ionizable lipid structures. This framework systematically benchmarks diverse molecular representations paired with a broad range of ML architectures spanning traditional models, feedforward neural networks, and state-of-the-art graph-based methods. In addition, the presented framework supports assessment of model generalization and evaluates prediction reliability beyond standard regression metrics. Using a curated dataset of 1,100 unique ionizable lipid structures derived from the HeLa transfection dataset originally reported by Xu et al., we show that within this framework, models leveraging explicit molecular substructure encoding consistently achieve the highest predictive accuracy and should serve as essential baselines for the development of new, more sophisticated models. In contrast, some current graph-based models, including AGILE, Chemprop, and KPGT, tend to show comparatively lower accuracy. The presented framework provides a standardized, transparent, and comprehensive benchmarking resource that enables meaningful comparison of emerging architectures and establishes strong baselines for future development of predictive models in lipid-based RNA delivery.

Asal Mehradfar, Mohammad Shahab Sepehri, Jose Miguel Hernandez-Lobato +4

Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA · Department of Engineering, University of Cambridge, Cambridge, UK · Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI +1

Graph Representation LearningMolecular Property PredictionMolecular Representation Learning
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May 3, 2025

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models

Data-driven molecular discovery leverages artificial intelligence/machine learning (AI/ML) and generative modeling to filter and design novel molecules. Discovering novel molecules requires accurate out-of-distribution (OOD) predictions, but ML models struggle to generalize OOD. Currently, no systematic benchmarks exist for molecular OOD prediction tasks. We present BOOM\mathbf{BOOM}, b\mathbf{b}enchmarks for o\mathbf{o}ut-o\mathbf{o}f-distribution m\mathbf{m}olecular property predictions: a chemically-informed benchmark for OOD performance on common molecular property prediction tasks. We evaluate over 150 model-task combinations to benchmark deep learning models on OOD performance. Overall, we find that no existing model achieves strong generalization across all tasks: even the top-performing model exhibited an average OOD error 3x higher than in-distribution. Current chemical foundation models do not show strong OOD extrapolation, while models with high inductive bias can perform well on OOD tasks with simple, specific properties. We perform extensive ablation experiments, highlighting how data generation, pre-training, hyperparameter optimization, model architecture, and molecular representation impact OOD performance. Developing models with strong OOD generalization is a new frontier challenge in chemical ML. This open-source benchmark is available at https://github.com/FLASK-LLNL/BOOM

Evan R. Antoniuk, Shehtab Zaman, Tal Ben-Nun +10

Lawrence Livermore National Laboratory, Livermore, CA · Binghamton University, Binghamton, NY

OOD GeneralizationMolecular Property PredictionMolecular Representation Learning
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Mar 16, 2025

GenShin: Guiding Rational Liposome Design by Ranking Liposomal Protein Corona through a Docking-Pose-Free GNN

Rational design of lipid nanoparticles (LNPs) for tissue-specific delivery critically depends on predicting the composition of the protein corona that forms on the lipid surface after intravenous administration. However, conventional characterization of the protein corona relies on costly and time-consuming mass spectrometry experiments, which require physically prepared liposome samples and therefore cannot serve as a pre-synthesis screening strategy for large candidate lipid spaces. The adsorption of plasma proteins onto liposomal surfaces is shaped by lipid chemical structures, protein properties and the biological environment, making this process difficult to simulate directly. In this work, we propose that scoring lipid-plasma protein pairs and ranking the resulting scores can provide a practical signal for revealing the relative composition of the liposomal surface protein corona.Here we introduce GenShin, a geometry-enhanced pose-free graph neural network designed to score lipid-plasma protein pairs. GenShin is pretrained on compound-protein affinity data to initialize a generalizable scoring function and is then fine-tuned on a rank fine-tuning dataset constructed from liposomal protein-corona abundance measurements to adapt the model to lipid-plasma protein pair scoring. Before fine-tuning, GenShin achieves competitive pose-free affinity prediction on the PDBbind v2016 benchmark compared with representative pose-dependent models. CASF-2016 perturbation experiments using the pretrained GenShin model further show that pose-dependent inference substantially degrades when intermolecular poses are unreliable, whereas GenShin remains stable without requiring such poses. This supports the practical advantage of GenShin for large-scale lipid-protein scoring.

Pingfei Zhu, Hongyi Liu, Xueyan Liu +2

Key Laboratory of symbolic Computation and Knowledge Engineering of ministry of education, Jilin University, Qianjin Street 2699, Changchun, Jilin Province, 130012, China · State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xue Yuan Road 38, Beijing 100191, China

Graph Neural NetworksMaterials ScienceMolecular Property Prediction
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Jan 3, 2025

Active Learning Enables Generation of Molecules that Advance the Known Pareto Front

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.

Evan R. Antoniuk, Peggy Li, Nathan Keilbart +3

Molecular OptimizationOOD GeneralizationActive LearningMolecular GenerationMolecular Property Prediction
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