ArticleJournal of cheminformatics2021
Diversity oriented Deep Reinforcement Learning for targeted molecule generation.
Article in Journal of cheminformatics, 2021. 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.
- Diversity Beats Size Scaling for Chemical Language Models.npj drug discovery · 2026Article
- Deep learning for small-molecule drug discovery: From molecular design to clinical translation.Journal of pharmaceutical analysis · 2026Review
- NaviDiv: a web app for monitoring chemical diversity in generative molecular design.Digital discovery · 2026Article
- Selector: A General Python Library for Diverse Subset Selection.Journal of chemical information and modeling · 2026Article
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- Selector: A General Python Library for Diverse Subset Selection.bioRxiv : the preprint server for biology · 2025Article
- Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach.Journal of pharmaceutical analysis · 2025Article
- A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules.Nature communications · 2025Article
- Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-Directed Molecular Generation.Journal of chemical information and modeling · 2025Article
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators.Journal of chemical information and modeling · 2024Article
- Hamiltonian diversity: effectively measuring molecular diversity by shortest Hamiltonian circuits.Journal of cheminformatics · 2024Article
- Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening.Journal of chemical information and modeling · 2024Article
- TransGEM: a molecule generation model based on Transformer with gene expression data.Bioinformatics (Oxford, England) · 2024Article
- Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design.Journal of chemical theory and computation · 2023Article
- LOGICS: Learning optimal generative distribution for designing de novo chemical structures.Journal of cheminformatics · 2023Article
- Combatting over-specialization bias in growing chemical databases.Journal of cheminformatics · 2023Article
- A Reinforcement Learning Framework to Discover Natural Flavor Molecules.Foods (Basel, Switzerland) · 2023Article
- 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
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Authors and funding
4 authors.
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Abstract
In this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties. The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain fixed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far. To demonstrate the effectiveness of the method, the Generator is trained to design molecules with an optimized coefficient of partition and also high inhibitory power against the Adenosine [Formula: see text] and [Formula: see text] opioid receptors. The results reveal that the model can effectively adjust the newly generated molecules towards the wanted direction. More importantly, it was possible to find promising sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.
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