ArticleJournal of computational chemistry2022
A molecular evolution algorithm for ligand design in DOCK.
Article in Journal of computational chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 18 citations in OpenAlex.
- DOCKweb: a web-based GUI platform for molecular modeling with DOCK6.Journal of computer-aided molecular design · 2026Article
- TaLiRAGen: target-aware ligand generation via retrieval-augmented large language models.Molecular diversity · 2026Article
- Expanding the Palette of Molecular Fragments for Small Molecule De Novo Design Through Isosteric Swapping.Journal of computational chemistry · 2026Article
- Hands-On Docking with Molegro Virtual Docker.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Benchmarking Molecular Mutation Operators for Evolutionary Drug Design.International journal of molecular sciences · 2025Article
- Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations.Journal of cheminformatics · 2025Article
- Substituents introduction of methyl and methoxy functional groups on resveratrol stabilizes mTOR binding for autophagic cell death induction.Scientific reports · 2025Article
- Fragment and torsion biasing algorithms for construction of small organic molecules in proteins using DOCK.Journal of computational chemistry · 2025Article
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- Recent Advances in Automated Structure-Based De Novo Drug Design.Journal of chemical information and modeling · 2024Review
- Geometric Deep Learning for Structure-Based Ligand Design.ACS central science · 2023Article
- Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit.Journal of chemical information and modeling · 2023Article
- A molecular evolution algorithm for ligand design in DOCK.Journal of computational chemistry · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
As a complement to virtual screening, de novo design of small molecules is an alternative approach for identifying potential drug candidates. Here, we present a new 3D genetic algorithm to evolve molecules through breeding, mutation, fitness pressure, and selection. The method, termed DOCK_GA, builds upon and leverages powerful sampling, scoring, and searching routines previously implemented into DOCK6. Three primary experiments were used during development: Single-molecule evolution evaluated three selection methods (elitism, tournament, and roulette), in four clinically relevant systems, in terms of mutation type and crossover success, chemical properties, ensemble diversity, and fitness convergence, among others. Large scale benchmarking assessed performance across 651 different protein-ligand systems. Ensemble-based evolution demonstrated using multiple inhibitors simultaneously to seed growth in a SARS-CoV-2 target. Key takeaways include: (1) The algorithm is robust as demonstrated by the successful evolution of molecules across a large diverse dataset. (2) Users have flexibility with regards to parent input, selection method, fitness function, and molecular descriptors. (3) The program is straightforward to run and only requires a single executable and input file at run-time. (4) The elitism selection method yields more tightly clustered molecules in terms of 2D/3D similarity, with more favorable fitness, followed by tournament and roulette.
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Registered trials
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