ArticleJournal of chemical information and modeling2023
Conformer Generation for Structure-Based Drug Design: How Many and How Good?
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The trial behind it
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Who cites it
25 citing papers in PubMed.
- In Silico to In Cerebro-Development of the Orexin Receptor Antagonist "Photorexant" for Photomodulation In Vitro and in Mouse Brains.Angewandte Chemie (International ed. in English) · 2026Article
- An extension of jamdock-suite for virtual screening from raw chemical datasets.STAR protocols · 2026Article
- Quantum Chemical Insights into Antibiotic Structure-Activity Relationships and Mechanisms of Action: A Review.Molecules (Basel, Switzerland) · 2026Review
- 3DICE: interpretable 3D cross-modal learning for drug-target interaction prediction and large-scale drug discovery.Bioinformatics (Oxford, England) · 2026Article
- Revisiting Target-Aware de novo Molecular Generation with TarPass: Between Rational Design and Texas Sharpshooter.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- 3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table.Journal of chemical information and modeling · 2026Article
- Dihydroquercetin in Obesity and Prediabetes: Case Report and Insights from Molecular Modeling.International journal of molecular sciences · 2026Article
- Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry.Journal of chemical information and modeling · 2026Article
- Polarity-determined triple partition of dual binding pockets unlocks mechanistic insights and a novel antagonist design of NMDA receptors: a combined MD/DFT study.RSC advances · 2026Article
- Pharmacophore-driven kinase profiling applied to the PKIS2 chemogenomic dataset.Scientific reports · 2026Article
- UPLC-Q-TOF-MS/MS and Network Pharmacology Approaches to Explore the Active Compounds and Mechanisms ofInternational journal of molecular sciences · 2026Article
- Natural Product-like Fragments Unlock Novel Chemotypes for a Kinase Target─Exploring Options beyond the Flatland.Journal of chemical information and modeling · 2026Article
- Probing curcumin reactive conformers in keto-enol tautomerization enhanced by clustering with t-SNE.Journal of molecular modeling · 2026Article
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- TEMPL: A Template-Based Protein-Ligand Pose Prediction Baseline.Journal of chemical information and modeling · 2025Article
- Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules.Journal of chemical theory and computation · 2025Article
- Synthon-Based Strategies Exploiting Molecular Similarity and Protein-Ligand Interactions for Efficient Screening of Ultra-Large Chemical Libraries.Journal of chemical information and modeling · 2025Article
- The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025Review
- Understanding Conformation Importance in Data-Driven Property Prediction Models.Journal of chemical information and modeling · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is fundamental to structure-based drug design. Conformational ensembles are required for rigid-body matching algorithms, such as shape-based or pharmacophore approaches, and even methods that treat the ligand flexibly, such as docking, are dependent on the quality of the provided conformations due to not sampling all degrees of freedom (e.g., only sampling torsions). Here, we empirically elucidate some general principles about the size, diversity, and quality of the conformational ensembles needed to get the best performance in common structure-based drug discovery tasks. In many cases, our findings may parallel "common knowledge" well-known to practitioners of the field. Nonetheless, we feel that it is valuable to quantify these conformational effects while reproducing and expanding upon previous studies. Specifically, we investigate the performance of a state-of-the-art generative deep learning approach versus a more classical geometry-based approach, the effect of energy minimization as a postprocessing step, the effect of ensemble size (maximum number of conformers), and construction (filtering by root-mean-square deviation for diversity) and how these choices influence the ability to recapitulate bioactive conformations and perform pharmacophore screening and molecular docking.
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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.