ArticleFrontiers in molecular biosciences2020
RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features.
Article in Frontiers in molecular biosciences, 2020. 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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16 citing papers in PubMed.
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- CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2.Journal of chemical information and modeling · 2026Article
- Systematic Search for Blood-Brain Barrier Modulating Peptides Based on Exhaustive E-Cadherin Domain-Domain Docking.Journal of chemical information and modeling · 2025Article
- In search of a photoswitchable drug for serotonin receptors: a molecular dynamics simulation study.RSC advances · 2025Article
- Discovery of novel HBV core protein inhibitors by high throughput virtual screening.Scientific reports · 2025Article
- Exploring chemical space for "druglike" small molecules in the age of AI.Frontiers in molecular biosciences · 2025Review
- Network Pharmacology, Molecular Docking andCurrent computer-aided drug design · 2025Article
- Identification of novel 3-dehydroquinate dehydratase (DHQD) inhibitors for anti-tuberculosis activity: insights from virtual screening, molecular docking, and dynamics simulations.In silico pharmacology · 2025Article
- Impact of MTHFD2 Expression in Bladder/Breast Cancer and Screening of Its Potential Inhibitor.ACS omega · 2024Article
- Advances in ligand-specific biosensing for structurally similar molecules.Cell systems · 2023Review
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.Briefings in bioinformatics · 2022Review
- Theoretical Studies on the Molecular Properties, Toxicity, and Biological Efficacy of 21 New Chemical Entities.ACS omega · 2021Article
- RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features.Frontiers in molecular biosciences · 2020Article
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5 authors.
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Abstract
The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally docking ligands into a protein binding site of interest, but this has the drawback of a large number of poses that must be evaluated to obtain accurate estimates of protein-ligand binding affinity. We here introduce a fast pre-filtering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket. Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance that is better than that of the original RASPD method and traditional scoring functions on a range of different test sets without the need for generating ligand poses. Additionally, we use RASPD+ to identify molecular features important for binding affinity and assess the ability of RASPD+ to enrich active molecules from decoys.
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