ArticleJournal of cheminformatics2020
CReM: chemically reasonable mutations framework for structure generation.
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 33 papers.
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Who cites it
33 citing papers in PubMed.
- The impact of reward scalarization and weight scheduling on optimization dynamics in multi-objective molecular design.Journal of cheminformatics · 2026Article
- PyMolGen: Database-Driven Molecular Generation of Drug-Like Compounds.Journal of chemical information and modeling · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A Reinforcement Learning-Guided Genetic Algorithm Integrating Medicinal Chemistry-Inspired Molecular Transformations.Journal of chemical information and modeling · 2026Article
- CReM-pharm: de novo 3D pharmacophore-based design with synthetic accessibility awareness.Journal of cheminformatics · 2026Article
- Article
- Qsarna: An Online Tool for Smart Chemical Space Navigation in Drug Design.Journal of chemical information and modeling · 2025Article
- Leveraging tree-transformer VAE with fragment tokenization for high-performance large chemical model generation.Communications chemistry · 2025Article
- Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations.Journal of cheminformatics · 2025Article
- Machine Learning-Driven Discovery of Structurally Related Natural Products as Activators of the Cardiac Calcium Pump SERCA2a.ChemMedChem · 2025Article
- Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease.Digital discovery · 2025Article
- Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation.Nature communications · 2025Article
- Improving Molecular Design with Direct Inverse Analysis of QSAR/QSPR Model.Molecular informatics · 2025Article
- MolPLA: a molecular pretraining framework for learning cores, R-groups and their linker joints.Bioinformatics (Oxford, England) · 2024Article
- t-SMILES: a fragment-based molecular representation framework for de novo ligand design.Nature communications · 2024Article
- MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design.Journal of cheminformatics · 2024Article
- Molecule auto-correction to facilitate molecular design.Journal of computer-aided molecular design · 2024Article
- Molecular fragmentation as a crucial step in the AI-based drug development pathway.Communications chemistry · 2024Review
- A molecule perturbation software library and its application to study the effects of molecular design constraints.Journal of cheminformatics · 2023Article
- Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search.Journal of chemical information and modeling · 2023Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.
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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.