ArticleJournal of cheminformatics2022
Systemic evolutionary chemical space exploration for drug discovery.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 28 citations in OpenAlex.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Multi-Property De Novo Drug Design Using Deep Learning-Based Knowledge Distillation and Reinforcement Learning.International journal of molecular sciences · 2026Article
- ReLink-PyB: an adapted REINVENT-based framework for non-DSM PfDHODH inhibitor discovery.Scientific reports · 2026Article
- TaLiRAGen: target-aware ligand generation via retrieval-augmented large language models.Molecular diversity · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Computational Design of Drugs for Epilepsy using a Novel Guided Evolutionary Algorithm for Enhanced Blood Brain Barrier Permeability.Central nervous system agents in medicinal chemistry · 2026Article
- Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations.Journal of cheminformatics · 2025Article
- Synthesis of Diazacyclic and Triazacyclic Small-Molecule Libraries Using Vicinal Chiral Diamines Generated from Modified Short Peptides and Their Application for Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Artificial Intelligence-Driven Computational Approaches in the Development of Anticancer Drugs.Cancers · 2024Article
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening.Journal of chemical information and modeling · 2024Article
- Paradigm shift required for translational research on the brain.Experimental & molecular medicine · 2024Review
- Recent Advances in Automated Structure-Based De Novo Drug Design.Journal of chemical information and modeling · 2024Review
- Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit.Journal of chemical information and modeling · 2023Review
- Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade.Pharmaceuticals (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a "lego-building" process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.
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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.