Molecule Design

Latest papers

43 papers11 papers in the last four weeks, up 175% on the four weeks before. 0.1% of all new papers.

ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
May 12, 2026Andrew Y. Zhou, Sharvaree Vadgama, Sumanth Varambally +3Drug Discovery AI Agents for Scientific Discovery
Department of Computer Science and Engineering, UC San Diego, La Jolla, California, United States · Department of Computer Science, Stanford University, Stanford, California · Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California
MolWorld: Molecule World Models for Actionable Molecular Optimization
May 9, 2026Yang Qiao, Bo Pan, Hao-Wei Pang +3Molecular Optimization World Models
Emory University · Atlanta, GA, USA · Merck & Co., Inc. +1
SMolLM: Small Language Models Learn Small Molecular Grammar
May 7, 2026Akhil Jindal, Harang JuSmall Language Models Mechanistic Interpretability
Moku
Mol-Debate: Multi-Agent Debate Improves Structural Reasoning in Molecular Design
Apr 22, 2026Wengyu Zhang, Xiao-Yong Wei, Qing LiAI-Assisted Scientific Research Multi-Agent Debate
♠The Hong Kong Polytechnic University · ♡Sichuan University
Associative Constructive Evolution: Enhancing Metaheuristics through Hebbian-Learned Generative Guidance
Mar 31, 2026Shanxian Lin, Yuichi Nagata, Haichuan YangMolecule Design Metaheuristic Optimization
Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima, Japan
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
Trust-Region Noise Search for Black-Box Alignment of Diffusion and Flow Models
Mar 15, 2026Niklas Schweiger, Daniel Cremers, Karnik RamDiffusion Model Alignment Black-Box Optimization
Technical University of Munich, Germany · Munich Center for Machine Learning, Germany
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
Time-Aware Latent Space Bayesian Optimization
Mar 1, 2026Tuan A. Vu, Julien Martinelli, Harri LähdesmäkiMolecule Design Bayesian Optimization
Aalto University
Active Flow Matching
Mar 1, 2026Yashvir S. Grewal, Daniel M. Steinberg, Thang D. Bui +2Molecule Design Flow-Based Generative Modeling
School of Computing, College of Systems and Society, Australian National University, Australia · Data61, CSIRO, Australia
Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
Feb 18, 2026Zifan Wang, Riccardo De Santi, Xiaoyu Mo +3Molecule Design Fine-Tuning
KTH Royal Institute of Technology · ETH Zurich · ETH AI Center +2
MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design
Nov 10, 2024Raul Ortega-Ochoa, Tejs Vegge, Jes Frellsen3D Molecular Generation Autoregressive Generation
Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark · CAPeX Pioneer Center for Accelerating P2X Materials Discovery · Department of Applied Mathematics and Computer Science, Technical University of Denmark

May 12, 2026J/K move · Enter open · S save

ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery

Andrew Y. Zhou, Sharvaree Vadgama, Sumanth Varambally, Peter Eckmann+2

Department of Computer Science and Engineering, UC San Diego, La Jolla, California, United States · Department of Computer Science, Stanford University, Stanford, California · Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California

Drug Discovery AI Agents for Scientific Discovery Multi-Objective Evolutionary Optimization AI-Assisted Scientific Research LLM Tool Use Molecular Generation+2

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Advances in large language models (LLMs) have recently opened new and promising avenues for small-molecule drug discovery. Yet existing LLM-based approaches for molecular generation often suffer from high rates of invalid and low-quality ligand candidates, a result of the syntactic limitations of current models with regard to molecular strings. In this paper, we introduce

\texttt{ToolMol}

, an evolutionary agentic framework for de novo drug design.

\texttt{ToolMol}

combines a multi-objective genetic algorithm with an agentic LLM operator that iteratively updates the ligand population. We build a comprehensive toolbox of RDKit-backed functions that allows our agentic operator to consisently make precise ligand modifications.

\texttt{ToolMol}

achieves state-of-the-art performance on multi-objective property optimization tasks, discovering drug-like and synthesizable ligands that have

>10\%

stronger predicted binding affinity compared to existing methods, evaluated on three protein targets.

\texttt{ToolMol}

ligands additionally achieve state-of-the-art results in gold-standard Absolute Binding Free Energy scores, gaining over existing methods by over

35\%

. By studying chain-of-thought reasoning traces, we observe that tool-calling enables the model to more faithfully execute its planned modifications, efficiently exploiting the strong chemical prior knowledge in LLMs.