ArticleJournal of cheminformatics2020
DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Multi-Property De Novo Drug Design Using Deep Learning-Based Knowledge Distillation and Reinforcement Learning.International journal of molecular sciences · 2026Article
- Molecule generation for drug design: A graph learning perspective.Fundamental research · 2026Review
- Generative artificial intelligence based models optimization towards molecule design enhancement.Journal of cheminformatics · 2025Review
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder with differentiable edge sampling.Biology direct · 2025Article
- A data-driven generative strategy to avoid reward hacking in multi-objective molecular design.Nature communications · 2025Article
- Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design.Journal of chemical theory and computation · 2023Article
- Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance.Journal of chemical information and modeling · 2023Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- AI in drug discovery and its clinical relevance.Heliyon · 2023Review
- Computer-aided multi-objective optimization in small molecule discovery.Patterns (New York, N.Y.) · 2023Review
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
- Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search.Journal of chemical information and modeling · 2022Article
- Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation.Journal of cheminformatics · 2022Article
- MO-MEMES: A method for accelerating virtual screening using multi-objective Bayesian optimization.Frontiers in medicine · 2022Article
- MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra.Biomolecules · 2021Article
- Intelligent host engineering for metabolic flux optimisation in biotechnology.The Biochemical journal · 2021Review
- Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System.International journal of molecular sciences · 2021Article
- The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes.Molecules (Basel, Switzerland) · 2021Review
- Optimizing blood-brain barrier permeation through deep reinforcement learning for de novo drug design.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.