ArticleJournal of chemical information and modeling2022
On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Fragment-Based Discovery of KLK6 and KLK7 Inhibitors.Journal of chemical information and modeling · 2026Article
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
- CCVAN Leverages Conditional Molecular Generation Through Conditional VAE and Wasserstein GAN.Interdisciplinary sciences, computational life sciences · 2026Article
- Revisiting Target-Aware de novo Molecular Generation with TarPass: Between Rational Design and Texas Sharpshooter.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Molecular Electrostatic Surface Potential: A Predictive Framework for Noncovalent Interactions and Adsorption Characteristics in Molecular Entities.International journal of molecular sciences · 2026Review
- GNN-MA: Soft Molecular Alignment with Cross-Graph Attention for Ligand-Based Virtual Screening.Molecules (Basel, Switzerland) · 2026Article
- The Heterocycle Isostere Explorer: A Computational Tool for the Discovery of Novel Aromatic Heterocyclic Isosteres.Journal of medicinal chemistry · 2026Article
- In Silico Analysis of Potential Stabilizer Binding Sites at Protein-RNA Interfaces.Computational and structural biotechnology journal · 2026Article
- In-silico assessment of phytochemical derivatives generated using CHEESE webserver for advancement of druggable candidate in pancreatic cancer therapy.In silico pharmacology · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- GESim: ultrafast graph-based molecular similarity calculation via von Neumann graph entropy.Journal of cheminformatics · 2025Article
- Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning.Critical reviews in toxicology · 2024Review
- Predicting the Hallucinogenic Potential of Molecules Using Artificial Intelligence.ACS chemical neuroscience · 2024Article
- Incorporating Synthetic Accessibility in Drug Design: Predicting Reaction Yields of Suzuki Cross-Couplings by Leveraging AbbVie's 15-Year Parallel Library Data Set.Journal of the American Chemical Society · 2024Article
- Average Electron Density: A Quantitative Tool for Evaluating Non-Classical Bioisosteres of Amides.ACS omega · 2024Article
- Feasibility of the inhibitor development for cancer: A systematic approach for drug design.PloS one · 2024Article
- 3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors.Journal of cheminformatics · 2023Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiparameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compound design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the generation of novel molecules with optimal properties. As before, the generative process outputs optimized molecules similar to the input structures, now with the improved feature of replacing parts of these molecules with fragments of similar three-dimensional (3D) shape and electrostatics. We developed and benchmarked a new python package, ESP-Sim, for the comparison of the electrostatic potential and the molecular shape, allowing the calculation of high-quality partial charges (e.g., RESP with B3LYP/6-31G**) obtained using the quantum chemistry program Psi4. By performing comparisons of 3D fragments, we can simulate 3D properties while overcoming the notoriously difficult step of accurately describing bioactive conformations. The new improved generative (DeepFMPO v3D) method is demonstrated with a scaffold-hopping exercise identifying CDK2 bioisosteres. The code is open-source and freely available.
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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.