Molecular Optimization

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Molecular optimization searches for molecules that meet goals such as stronger target binding, better drug-like properties, or improved material performance, often while preserving a starting scaffold or satisfying synthesis constraints. Methods include evolutionary search and Bayesian optimization, as well as graph, sequence, and 3D generative models—such as diffusion and language models—guided by property predictors, docking scores, or reinforcement learning. Recent work also uses tool-using agents, fragment-based edits, and closed-loop experiments or simulations to handle multiple objectives and limited evaluation budgets; methods are commonly compared on suites such as PMO, GuacaMol, TDC, and CrossDocked. A central challenge is that gains on predicted properties or standard benchmarks may not transfer to experiments or new targets, while stronger optimization can reduce diversity, synthesizability, or similarity to useful starting molecules.

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103 papers17 papers in the last four weeks, up 113% on the four weeks before. 0.1% of all new papers.

Deciphering Scientific Reasoning Steps from Outcome Data for Molecule Optimization
Mar 13, 2026Zequn Liu, Kehan Wu, Shufang Xie +5Scientific Reasoning in Language Models Molecular Optimization
Zhongguancun Academy · School of Computer Science and Technology, University of Science and Technology of China · Global Health Drug Discovery Institute
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
Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning
Mar 6, 2026Xuan Li, Zhanke Zhou, Zongze Li +4Molecular Optimization Large Language Model-Guided Optimization
Hong Kong Baptist University · CMIC, Shanghai Jiao Tong University · DAMO Academy, Alibaba Group +1
AI4S-SDS: A Neuro-Symbolic Solvent Design System via Sparse MCTS and Differentiable Physics Alignment
Mar 4, 2026Jiangyu ChenLarge Language Model-Guided Scientific Discovery Neuro-Symbolic AI
State Key Laboratory for Novel Software Technology at Nanjing University, School of Computer Science, Nanjing University · Suzhou Laboratory
MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery
Mar 3, 2026Maksim Kuznetsov, Zulfat Miftahutdinov, Rim Shayakhmetov +17Molecular Optimization Molecular Property Prediction
Insilico Medicine · Liquid AI
Retrieval-Augmented Foundation Models for Matched Molecular Pair Transformations to Recapitulate Medicinal Chemistry Intuition
Feb 18, 2026Bo Pan, Peter Zhiping Zhang, Hao-Wei Pang +4Molecular Generation Drug Discovery
Department of Computer Science, Emory University, Atlanta, GA, USA · Merck & Co., Inc., Rahway, NJ, USA
Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer
Feb 17, 2026Hayato Kunugi, Mohsen Rahmani, Yosuke Iyama +8Molecular Generation Quantum Generative Modeling
Inovation to Implementation Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, 569-1125, Japan. · D-Wave Systems Inc., 3033 Beta Ave, Burnaby, V5G 4M9, Canada.
FRAGMENTA: Efficient End-to-end Fragmentation-based Generative Model with Agentic Tuning for Drug Lead Optimization in Small Data Regime
Nov 25, 2025Yuto Suzuki, Paul Awolade, Daniel V. LaBarbera +1Molecular Generation Molecular Optimization
Department of Computer Science and Engineering University of Colorado Denver Denver, CO, USA · Skaggs School of Pharmacy University of Colorado Anschutz Medical Campus Aurora, CO, USA
STAR-VAE: A Scalable Latent-Variable Transformer for Controllable Molecular Generation
Nov 4, 2025Bum Chul Kwon, Ben Shapira, Moshiko Raboh +7Molecular Optimization Transformer
IBM Research, Cambridge, MA, USA · IBM Research, Haifa, Israel · IBM Research, Yorktown Heights, NY, USA
RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics
Oct 14, 2025Danqi Liao, Chen Liu, Xingzhi Sun +8Molecular Generation Molecular Optimization
Yale University
Generative molecule evolution using 3D pharmacophore for efficient Structure-Based Drug Design
Jul 27, 2025Yi He, Ailun Wang, Zhi Wang +3Structure-Based Drug Design Molecular Generation
ByteDance Seed
Active Learning Enables Generation of Molecules that Advance the Known Pareto Front
Jan 3, 2025Evan R. Antoniuk, Peggy Li, Nathan Keilbart +3Molecular Optimization OOD Generalization
Small Molecule Optimization with Large Language Models
Jul 26, 2024Philipp Guevorguian, Menua Bedrosian, Tigran Fahradyan +3Molecular Optimization Evolutionary Optimization
YerevaNN research lab, Yerevan, Armenia.

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Deciphering Scientific Reasoning Steps from Outcome Data for Molecule Optimization

Zequn Liu, Kehan Wu, Shufang Xie, Zekun Guo+4

Zhongguancun Academy · School of Computer Science and Technology, University of Science and Technology of China · Global Health Drug Discovery Institute

Scientific Reasoning in Language Models Molecular Optimization Large Language Model-Guided Optimization LLM Reasoning Drug Discovery

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Emerging reasoning models hold promise for automating scientific discovery. However, their training is hindered by a critical supervision gap: experimental outcomes are abundant, whereas intermediate reasoning steps are rarely documented at scale. To bridge this gap, we propose DESRO, a framework for deciphering scientific reasoning from outcomes. By analyzing shared patterns and key differences within grouped data, a large language model (LLM) can recover the underlying logic. We instantiate this framework in molecule optimization, a pivotal stage in drug discovery that traditionally relies on the iterative reasoning of medicinal chemists. Across 2.3 million molecular property records, our framework infers optimization rationales by grouping molecules with shared fragments, then using an LLM to analyze how structural variations correlate with property differences. Based on the derived data, we train a model that conducts molecule optimization through an interpretable reasoning process. DESRO achieves the highest success rates on 15 out of 18 tasks, spanning both single- and multi-property optimization of bioactivity and ADMET properties. The reasoning process enables robust generalization to out-of-distribution scenarios, including novel property combinations, unseen biological targets, and unseen properties defined solely by natural language descriptions. In retrospective case studies under strict temporal splits, the model autonomously reconstructs expert-level lead optimization trajectories. Additionally, our framework extends beyond molecule optimization to reaction ligand selection. Our results establish deciphering reasoning steps from outcome data as a viable paradigm for enabling scientific reasoning, providing a scalable approach to accelerate scientific discovery.