ArticleJournal of chemical information and modeling2015
Strategy to discover diverse optimal molecules in the small molecule universe.
Article in Journal of chemical information and modeling, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Complete Computational Exploration of Eight-Carbon Hydrocarbon Chemical Space.The Journal of organic chemistry · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Toward Environment-Sensitive Molecular Inference via Mixed Integer Linear Programming.ACS omega · 2025Article
- Cycle-configuration descriptors: a novel graph-theoretic approach to enhancing molecular inference.Journal of cheminformatics · 2025Article
- A dynamic programming algorithm for generating chemical isomers based on frequency vectors.Scientific reports · 2025Article
- Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations.Journal of cheminformatics · 2025Article
- Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators.Journal of chemical information and modeling · 2024Article
- Quadratic descriptors and reduction methods in a two-layered model for compound inference.Frontiers in genetics · 2024Article
- Generative Models as an Emerging Paradigm in the Chemical Sciences.Journal of the American Chemical Society · 2023Review
- Deep Learning and Computational Chemistry.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Has Artificial Intelligence Impacted Drug Discovery?Methods in molecular biology (Clifton, N.J.) · 2022Article
- Artificial Intelligence for Autonomous Molecular Design: A Perspective.Molecules (Basel, Switzerland) · 2021Review
- Exploring and mapping chemical space with molecular assembly trees.Science advances · 2021Article
- Black Phosphorus, an Emerging Versatile Nanoplatform for Cancer Immunotherapy.Pharmaceutics · 2021Review
- A novel method for inference of acyclic chemical compounds with bounded branch-height based on artificial neural networks and integer programming.Algorithms for molecular biology : AMB · 2021Article
- An Inverse QSAR Method Based on a Two-Layered Model and Integer Programming.International journal of molecular sciences · 2021Article
- Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.Biotechnology and bioprocess engineering : BBE · 2020Review
- Molecular generative model based on conditional variational autoencoder for de novo molecular design.Journal of cheminformatics · 2018Article
- Charge-Transfer Knowledge Graph among Amino Acids Derived from High-Throughput Electronic Structure Calculations for Protein Database.ACS omega · 2018Article
- Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.ACS central science · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The small molecule universe (SMU) is defined as a set of over 10(60) synthetically feasible organic molecules with molecular weight less than ∼500 Da. Exhaustive enumerations and evaluation of all SMU molecules for the purpose of discovering favorable structures is impossible. We take a stochastic approach and extend the ACSESS framework ( Virshup et al. J. Am. Chem. Soc. 2013 , 135 , 7296 - 7303 ) to develop diversity oriented molecular libraries that can generate a set of compounds that is representative of the small molecule universe and that also biases the library toward favorable physical property values. We show that the approach is efficient compared to exhaustive enumeration and to existing evolutionary algorithms for generating such libraries by testing in the NKp fitness landscape model and in the fully enumerated GDB-9 chemical universe containing 3 × 10(5) molecules.
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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.