ArticleJournal of chemical information and modeling2024
Evolutionary Multiobjective Molecule Optimization in an Implicit Chemical Space.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 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
- Integrated framework for targeting dynamic penicillin-binding protein 2a via ensemble structural biology and deep generative modeling.Frontiers in microbiology · 2026Article
- Multi-Objective Drug Molecule Optimization Based on Tanimoto Crowding Distance and Acceptance Probability.Pharmaceuticals (Basel, Switzerland) · 2025Article
- CMOMO: a deep multi-objective optimization framework for constrained molecular multi-property optimization.Briefings in bioinformatics · 2025Article
- Artificial Intelligence in Molecular Optimization: Current Paradigms and Future Frontiers.International journal of molecular sciences · 2025Review
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Authors and funding
8 authors.
Funding
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Abstract
Optimization techniques play a pivotal role in advancing drug development, serving as the foundation of numerous generative methods tailored to efficiently design optimized molecules derived from existing lead compounds. However, existing methods often encounter difficulties in generating diverse, novel, and high-property molecules that simultaneously optimize multiple drug properties. To overcome this bottleneck, we propose a multiobjective molecule optimization framework (MOMO). MOMO employs a specially designed Pareto-based multiproperty evaluation strategy at the molecular sequence level to guide the evolutionary search in an implicit chemical space. A comparative analysis of MOMO with five state-of-the-art methods across two benchmark multiproperty molecule optimization tasks reveals that MOMO markedly outperforms them in terms of diversity, novelty, and optimized properties. The practical applicability of MOMO in drug discovery has also been validated on four challenging tasks in the real-world discovery problem. These results suggest that MOMO can provide a useful tool to facilitate molecule optimization problems with multiple properties.
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Registered trials
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