ArticleJournal of chemical information and modeling2019
Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization.
Article in Journal of chemical information and modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed.
- Automated pharmacophore hypothesis generator for SILCS-Pharm: ph4gen.Journal of computer-aided molecular design · 2026Article
- Probe-Atom Distributions Obtained from Mixed-Solvent Molecular Dynamics Improve the Scoring of Docking Calculations.Journal of chemical information and modeling · 2026Article
- Docking-score landscapes shape active-learning performance across Vina, Glide, and SILCS.Journal of computer-aided molecular design · 2026Article
- Drude SILCS-Nucleic: harnessing explicit electronic polarization in targeting RNA and DNA for drug design.Nucleic acids research · 2026Article
- Arrhythmia risk predictions from molecular simulations of cardiac ion channel-drug interactions.Biophysical journal · 2026Article
- RinPy, a Python Package for Residue Interaction Network Model to Analyze Protein Structures and Predict Ligand Binding Sites.Journal of chemical information and modeling · 2026Article
- Structural insights enable drug discovery for the neuronal NBCn2 carbonate transporter.Nature communications · 2026Article
- Targeting conserved domains of hypoxia-inducible factors for cancer therapy.The Journal of experimental medicine · 2026Article
- Photoenzymatic Hydroalkylation Enables Streamlined Access to Aryl Glutarimide Precursors.Angewandte Chemie (International ed. in English) · 2026Article
- Article
- Computational Modeling of PROTAC Ternary Complexes as Ensembles Using SILCS-xTAC.Journal of chemical information and modeling · 2026Article
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- Drude SILCS-Nucleic: Harnessing Explicit Electronic Polarization in Targeting RNA and DNA for Drug Design.bioRxiv : the preprint server for biology · 2025Article
- CryoEM and computational modeling structural insights into the pH regulator NBCn1.Nature communications · 2025Article
- Prediction of TdP Arrhythmia Risk Through Molecular Simulations of Conformation-specific Drug Interactions with the hERG KbioRxiv : the preprint server for biology · 2025Article
- Review
- GaSal-2: A Water-Soluble Antipseudomonal Agent Targeting the Extracellular Hemophore HasAp.ACS infectious diseases · 2025Article
- High-Throughput Ligand Dissociation Kinetics Predictions Using Site Identification by Ligand Competitive Saturation.Journal of chemical theory and computation · 2025Article
- Potent and Selective Human Constitutive Androstane Receptor Activator DL5055 Facilitates Cyclophosphamide-Based Chemotherapies.Journal of medicinal chemistry · 2025Article
- Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set.Journal of chemical theory and computation · 2025Article
3 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
Chemical fragment cosolvent sampling techniques have become a versatile tool in ligand-protein binding prediction. Site-identification by ligand competitive saturation (SILCS) is one such method that maps the distribution of chemical fragments on a protein as free energy fields called FragMaps. Ligands are then simulated via Monte Carlo techniques in the field of the FragMaps (SILCS-MC) to predict their binding conformations and relative affinities for the target protein. Application of SILCS-MC using a number of different scoring schemes and MC sampling protocols against multiple protein targets was undertaken to evaluate and optimize the predictive capability of the method. Seven protein targets and 551 ligands with broad chemical variability were used to evaluate and optimize the model to maximize Pearson's correlation coefficient, Pearlman's predictive index, correct relative binding affinity, and root-mean-square error versus the absolute experimental binding affinities. Across the protein-ligand sets, the relative affinities of the ligands were predicted correctly an average of 69% of the time for the highest overall SILCS protocol. Training the FragMap weighting factors using a Bayesian machine learning (ML) algorithm led to an increase to an average 75% relative correct affinity predictions. Furthermore, once the optimal protocol is identified for a specific protein-ligand system average predictabilities of 76% are achieved. The ML algorithm is successful with small training sets of data (30 or more compounds) due to the use of physically correct FragMap weights as priors. Notably, the 76% correct relative prediction rate is similar to or better than free energy perturbation methods that are significantly computationally more expensive than SILCS. The results further support the utility of SILCS as a powerful and computationally accessible tool to support lead optimization and development in drug discovery.
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