Data to 7 Oct 2026

Molecular De Novo Design through Deep Reinforcement Learning

arXiv 1704.07555 ↗First posted 25 Apr 2017cs.AIcited by 264

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Abstract (arXiv)

This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.

Key facts. Cited by 264 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Apr 2017.

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Citing papers per year: 2020: 1; 2021: 33; 2022: 39; 2023: 34; 2024: 55; 2025: 48; 2026: 54.

264 papers.

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2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
2022Translation between Molecules and Natural Languagecited by 173
2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
2021Geometric Deep Learning on Molecular Representationscited by 113
2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
2021Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generationcited by 68
2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
2024Efficient Evolutionary Search Over Chemical Space with Large Language Modelscited by 59
2023DrugAssist: A Large Language Model for Molecule Optimizationcited by 57

38 more

2025GenMol: A Drug Discovery Generalist with Discrete Diffusioncited by 47
2022Reinforced Genetic Algorithm for Structure-based Drug Designcited by 46
2021Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spacescited by 45
2022Retrieval-based Controllable Molecule Generationcited by 44
2023Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Languagecited by 44
2025El Agente: An Autonomous Agent for Quantum Chemistrycited by 43
2025Feedback Descent: Open-Ended Text Optimization via Pairwise Comparisoncited by 42
2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40
2024RGFN: Synthesizable Molecular Generation Using GFlowNetscited by 39
2021Learning to design drug-like molecules in three-dimensional space using deep generative modelscited by 37
2024Genetic-guided GFlowNets for Sample Efficient Molecular Optimizationcited by 36
2021DOCKSTRING: easy molecular docking yields better benchmarks for ligand designcited by 33
2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 31
2022Computer-Aided Multi-Objective Optimization in Small Molecule Discoverycited by 31
2025KL-Regularized Reinforcement Learning is Designed to Mode Collapsecited by 29
2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
2021How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Moleculescited by 27
2024DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimizationcited by 27
2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
2024SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraintscited by 26
2024LICO: Large Language Models for In-Context Molecular Optimizationcited by 26
2021Off-Policy Reinforcement Learning with Delayed Rewardscited by 25
2022Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Modelcited by 25
2024Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trialscited by 24
2022Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenterscited by 22
2024Molecule Generation with Fragment Retrieval Augmentationcited by 22
2022Graph Neural Networks for Moleculescited by 21
2021Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attentioncited by 20
2022Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Designcited by 19
2023Drug Discovery with Dynamic Goal-aware Fragmentscited by 19
2023De novo Drug Design using Reinforcement Learning with Multiple GPT Agentscited by 19
2021GeneDisco: A Benchmark for Experimental Design in Drug Discoverycited by 18
2022Deep Reinforcement Learning for Inverse Inorganic Materials Designcited by 18
2023Searching for High-Value Molecules Using Reinforcement Learning and Transformerscited by 18
2024Generative Artificial Intelligence for Navigating Synthesizable Chemical Spacecited by 18
2025Graph Generative Pre-trained Transformercited by 16
2021C5T5: Controllable Generation of Organic Molecules with Transformerscited by 15
2024ACEGEN: Reinforcement learning of generative chemical agents for drug discoverycited by 14

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