Molecular Generation

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Molecular generation studies how to create new chemical structures or 3D conformations that meet goals such as binding a protein, having useful properties, or matching spectral evidence. Common approaches include autoregressive chemical language models, graph and 3D diffusion or flow-matching models, and fragment-based generators; reinforcement learning, search, and active learning can steer their outputs toward desired properties. Researchers compare methods on benchmarks such as QM9, ZINC, MOSES, CrossDocked, and MassSpecGym, evaluating validity, novelty, diversity, property performance, and structural fit. A central challenge is balancing goal-directed optimization with chemical plausibility, diversity, and synthesizability, while ensuring that gains on computational proxies hold up in independent or experimental tests.

Latest papers

235 papers32 papers in the last four weeks, up 60% on the four weeks before. 0.2% of all new papers.

Multi-Objective Reinforcement Learning for Generating Covalent Inhibitor Candidates
Apr 21, 2026Renee GilMolecular Optimization Molecular Generation
Independent Researcher
Structure-guided molecular design with contrastive 3D protein-ligand learning
Apr 21, 2026Carles Navarro, Philipp Tholke, Gianni de FabritiisContrastive Learning Virtual Screening
Acellera Labs, C/ Doctor Trueta 183, 08005 Barcelona, Spain · Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/ Doctor Aiguader 88, 08003 Barcelona, Spain · Instituci´o Catalana de Recerca i Estudis Avan¸cats (ICREA), Passeig Llu´ıs Companys 23, 08010 Barcelona, Spain
An Integrated Deep-Learning Framework for Peptide-Protein Interaction Prediction and Target-Conditioned Peptide Generation with ConGA-PepPI and TC-PepGen
Apr 20, 2026Chupei Tang, Junxiao Kong, Moyu Tang +5Molecular Generation Conditional Generative Modeling
School of Mathematics and Statistics, Lanzhou University, 222 South Tianshui Road, Lanzhou 730000, Gansu, China · Cuiying Honors College, Lanzhou University, Lanzhou, Gansu, China · Shanghai Innovation Institute, Shanghai, China +1
How Creative Are Large Language Models in Generating Molecules?
Apr 20, 2026Wen Tao, Yiwei Wang, Peng Zhou +6Creativity Assessment LLM Evaluation
Nanyang Technological University · University of California, Merced · Hunan University +1
RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design
Apr 19, 2026Meghana Kshirsagar, Allen Nie, Ching-An Cheng +5Inference-Time Search Large Language Model-Guided Optimization
AI for Good, Microsoft Redmond · Google DeepMind · Google Research +3
KinetiDiff: Docking-Guided Diffusion for De Novo ACVR1 Inhibitor Design in Fibrodysplasia Ossificans Progressiva
Apr 17, 2026Aaryan PatelMolecular Optimization Diffusion Guidance
Saugus High School, Santa Clarita, CA
FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time
Apr 17, 2026Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar +4Inference-Time Scaling Molecular Generation
Massachusetts Institute of Technology, Cambridge, MA, United States · Texas A&M University, College Station, TX, United States
BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization
Apr 15, 2026Jackie Rao, Ferran Gonzalez Hernandez, Leon Gerard +1Protein Design Surrogate-Assisted Optimization
MRC Biostatistics Unit, University of Cambridge, UK · Centre for AI, Data Science and Artificial Intelligence, R&D, AstraZeneca, Barcelona, Spain
CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation
Apr 13, 2026Yanting Li, Zhuoyang Jiang, Enyan Dai +3Molecular Generation Molecular Optimization
The Hong Kong University of Science and Technology (Guangzhou) · Jinan University, Guangzhou
Inverse Design of Inorganic Compounds with Generative AI
Apr 11, 2026Hannes Kneiding, Lucía Morán-González, Nishamol Kuriakose +2Inverse Design Molecular Generation
Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway
General Multimodal Protein Design Enables DNA-Encoding of Chemistry
Apr 6, 2026Jarrid Rector-Brooks, Théophile Lambert, Marta Skreta +15Protein Design Molecular Generation
California Institute of Technology · Mila – Québec AI Institute · Université de Montréal +9
Generative Chemical Language Models for Energetic Materials Discovery
Mar 30, 2026Andrew Salij, R. Seaton Ullberg, Megan C. Davis +5Molecular Generation Materials Science
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA · Weapon Stockpile Modernization Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA · Computing and Artificial Intelligence Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
Property-Guided Molecular Generation and Optimization via Latent Flows
Mar 27, 2026Alexander Arjun Lobo, Urvi Awasthi, Leonid ZhukovMolecular Generation Molecular Optimization
BCG X AI Science Institute
SIGMA: Semantic Identifier Grouping for Molecular Autoregression
Mar 26, 2026Xinyu Wang, Fei Dou, Jinbo Bi +1Molecular Generation Molecular Representation Learning
School of Computing, University of Connecticut, Storrs, CT, USA · Institute for Artificial Intelligence, University of Georgia, Athens, GA, USA · Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
Permutation-Symmetrized Diffusion for Unconditional Molecular Generation
Mar 24, 2026Gyeonghoon Ko, Juho LeeMolecular Generation Diffusion Models
Korea Advanced Institute of Science and Technology
Latent Style-based Quantum Wasserstein GAN for Drug Design
Mar 23, 2026Julien Baglio, Yacine Haddad, Richard PolifkaMolecular Generation Quantum Generative Modeling
Center for Quantum Computing and Quantum Coherence (QC2), Department of Physics, University of Basel, Klingelbergstrasse 82, CH-4056 Basel, Switzerland · QuantumBasel, Schorenweg 44b, CH-4144 Arlesheim, Switzerland · Laboratory for High Energy Physics (LHEP), Physics Institute, Faculty of Sciences, University of Bern, Sidlerstrasse 5, CH-3012 Bern, Switzerland +1
A chemical language model for reticular materials design
Mar 20, 2026Dhruv Menon, Vivek Singh, Xu Chen +7Chemical Language Models Materials Science
Department of Chemical Engineering & Biotechnology, University of Cambridge, Cambridge, UK · Department of Chemistry, University of California – Berkeley, Berkeley, CA, USA · Bakar Institute of Digital Materials for the Planet, Berkeley, CA, USA +2
MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models
Mar 18, 2026Philippe Formont, Maxime Darrin, Ismail Ben Ayed +1Molecular Generation Molecule Design
Universit´e Paris-Saclay · ´ETS Montr´eal · ILLS – International Laboratory on Learning Systems +4
PI-Mamba: Linear-Time Protein Backbone Generation via Spectrally Initialized Flow Matching
Mar 17, 2026Tianyu Wu, Lin ZhuProtein Structure Generation Protein Design
Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, 600 S Mathews Ave., 61801, IL, USA · School of Information Science, University of Illinois Urbana-Champaign, 501 E Daniel St., 61820, IL, USA
Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention
Mar 16, 2026Jeffrey D. VarnerHopfield Networks Molecular Generation
R.F. Smith School of Chemical and Biomolecular Engineering · Cornell University, Ithaca, NY 14850
A Multitask Large Reasoning Model for Molecular Science
Mar 13, 2026Pengfei Liu, Shuang Ge, Xiaobo Wang +6Scientific Reasoning Multi-Task Learning
Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, Southern Medical University, Guangzhou, Guangdong, 510515, China · Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518000, China · Guangdong Provincial Key Laboratory of Psychoactive Substances Monitoring and Safety, Anti-Drug Technology Center of Guangdong Province (National Anti-Drug Laboratory Guangdong Regional Center), Guangzhou, 510230, China +4
CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design
Mar 13, 2026Mansoor Ahmed, Nadeem Taj, Imdad Ullah Khan +2Benchmark Design Protein Binder Design
Georgia State University, Atlanta, GA, USA · Georgia Institute of Technology, Atlanta, GA, USA · University of Engineering and Technology, Lahore, Pakistan +1
Continuous Diffusion Transformers for Designing Synthetic Regulatory Elements
Mar 11, 2026Jonathan Liu, Kia GhodsMolecular Generation Diffusion Transformer
Department of Computer Science Princeton University
Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation
Mar 10, 2026Junyi An, Chao Qu, Yun-Fei Shi +33D Molecular Generation Molecular Generation
Shanghai Academy of Artificial Intelligence for Science, SAIS · Artificial Intelligence Innovation and Incubation (AI3) Institute, Fudan University
Logos: An evolvable reasoning engine for rational molecular design
Mar 10, 2026Haibin Wen, Zhe Zhao, Fanfu Wang +4Molecule Design AI for Science
City University of Hong Kong, Hong Kong 999077, China · Lanzhou University, Lanzhou 730000, China · Riltide Medicines, Beijing, China +1
ConfHit: Conformal Generative Design with Oracle Free Guarantees
Mar 7, 2026Siddhartha Laghuvarapu, Ying Jin, Jimeng SunMolecular Generation Weighted Conformal Prediction
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, IL, USA · Department of Statistics and Data Science, Wharton School, University of Pennysylvania, PA, USA
Inference-Time Toxicity Mitigation in Protein Language Models
Mar 4, 2026Manuel Fernández Burda, Santiago Aranguri, Iván Arcuschin Moreno +1Protein Language Models Inference-Time Language Model Alignment
Laboratory of Applied Artificial Intelligence (LIAA), Institute of Computer Sciences (ICC), CONICET - Universidad de Buenos · AI Safety Argentina (AISAR) · Goodfire +1
D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation
Mar 2, 2026Zhao Yang, Hengchang Liu, Chuan Cao +1Molecular Foundation Models Genomic Foundation Models
Gaoling School of Artificial Intelligence, Renmin University of China · Zhongguancun Academy
Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials
Feb 24, 2026Alex Morehead, Miruna Cretu, Antonia Panescu +143D Foundation Models Multimodal Foundation Models
LBNL · ICSI · University of Cambridge +3
De novo molecular structure elucidation from mass spectra via flow matching
Feb 23, 2026Ghaith Mqawass, Tuan Le, Fabian Theis +1Mass Spectrometry Molecular Generation
TUM School of Life Sciences Weihenstephan, Technical University of Munich, Germany · Machine Learning and Computational Sciences, Pfizer Research & Development, Berlin, Germany · TUM School of Computation, Information and Technology, Technical University of Munich, Germany +1

Apr 21, 2026J/K move · Enter open · S save

Multi-Objective Reinforcement Learning for Generating Covalent Inhibitor Candidates

Renee Gil

Independent Researcher

Molecular Optimization Molecular Generation RL for Generative Models Drug Discovery

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Rational design of covalent inhibitors requires simultaneously optimizing multiple properties, such as binding affinity, target selectivity, or electrophilic reactivity. This presents a multi-objective problem not easily addressed by screening alone. Here we present a machine learning pipeline for generating covalent inhibitor candidates using multi-objective reinforcement learning (RL), applied to two targets: epidermal growth factor receptor (EGFR) and acetylcholinesterase (ACHE). A SMILES-based pretrained LSTM serves as the generative model, optimized via policy gradient RL with Pareto crowding distance to balance competing scoring functions including synthetic accessibility, predicted covalent activity, residue affinity, and an approximated docking score. The pipeline rediscovers known covalent inhibitors at rates of up to 0.50% (EGFR) and 0.74% (ACHE) in 10,000-structure runs, with candidate structures achieving warhead-to-residue distances as short as 5.5 angstrom (EGFR) and 3.2 angstrom (ACHE) after further docking-based screening. More notably, the pipeline spontaneously generates structures bearing warhead motifs absent from the training data - including allenes, 3-oxo-

β

-sultams, and

α

-methylene-

β

-lactones - all of which have independent literature support as covalent warheads. These results suggest that RL-guided generation can explore covalent chemical space beyond its training distribution, and may be useful as a tool for medicinal chemists working on covalent drug discovery.