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Toward the Rational Design of p53-Stabilizing Drugs: Probing the Surface of the Oncogenic Y220C Mutant

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SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions

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Discovery of a Novel Family of CDK Inhibitors with the Program LIDAEUS

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Sampling of Organic Solutes in Aqueous and Heterogeneous Environments Using Oscillating Excess Chemical Potentials in Grand Canonical-like Monte Carlo-Molecular Dynamics Simulations

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Druggable Protein Interaction Sites Are More Predisposed to Surface Pocket Formation than the Rest of the Protein Surface

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Structural basis for GPR40 allosteric agonism and incretin stimulation

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Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization

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Using Ligand‐Mapping Simulations to Design a Ligand Selectively Targeting a Cryptic Surface Pocket of Polo‐Like Kinase 1

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Discovery of 6-(2,4-Difluorophenoxy)-2-[3-hydroxy-1-(2-hydroxyethyl)propylamino]-8-methyl-8 H -pyrido[2,3- d ]pyrimidin-7-one (Pamapimod) and 6-(2,4-Difluorophenoxy)-8-methyl-2-(tetrahydro-2 H -pyran-4-ylamino)pyrido[2,3- d ]pyrimidin-7(8 H )-one (R1487) as Orally Bioavailable and Highly Selective Inhibitors of p38α Mitogen-Activated Protein Kinase

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Identification of Cryptic Binding Sites Using MixMD with Standard and Accelerated Molecular Dynamics

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Exploring Cryptic Pockets Formation in Targets of Pharmaceutical Interest with SWISH

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Identification and characterization of fragment binding sites for allosteric ligand design using the site identification by ligand competitive saturation hotspots approach (SILCS-Hotspots)

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Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction

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Residues Distant from the Active Site Influence Protein-tyrosine Phosphatase 1B Inhibitor Binding

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Rapid and accurate estimation of protein–ligand relative binding affinities using site-identification by ligand competitive saturation

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MixMD Probeview: Robust Binding Site Prediction from Cosolvent Simulations

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Structural Basis for Activation of the Heterodimeric GABAB Receptor

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Featurization strategies for protein–ligand interactions and their applications in scoring function development

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Probing acid replacements of thiophene PTP1B inhibitors

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Structural Mechanism for the Specific Assembly and Activation of the Extracellular Signal Regulated Kinase 5 (ERK5) Module

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Small Molecule Binding to Proteins: Affinity and Binding/Unbinding Dynamics from Atomistic Simulations

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pMD-Membrane: A Method for Ligand Binding Site Identification in Membrane-Bound Proteins

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Computational Analysis of Protein Hotspots

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Mixed-Probe Simulation and Probe-Derived Surface Topography Map Analysis for Ligand Binding Site Identification

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SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules

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SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein–Protein Interfaces

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An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening

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Exploring the Application of SiteMap and Site Finder for Focused Cryptic Pocket Identification

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Discovery of a novel allosteric inhibitor-binding site in ERK5: comparison with the canonical kinase hinge ATP-binding site

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Accessible Color Sequences for Data VisualizationPreprint

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SiteFerret: Beyond Simple Pocket Identification in Proteins

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Open–close structural change upon ligand binding and two magnesium ions required for the catalysis of N-acetylhexosamine 1-kinase

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Identification of small-molecule protein–protein interaction inhibitors for NKG2D

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GPU‐specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations

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Comparison of in silico tools for binding site prediction applied for structure-based design of autolysin inhibitors

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Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms

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Colabind: A Cloud-Based Approach for Prediction of Binding Sites Using Coarse-Grained Simulations with Molecular Probes

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Drug Design Data Resource Grand Challenge 2 Dataset: FXR - Farnesoid X receptor

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CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological Data Analysis

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Exploring Druggable Binding Sites on the Class A GPCRs Using the Residue Interaction Network and Site Identification by Ligand Competitive Saturation

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CrypTothML: An Integrated Mixed-Solvent Molecular Dynamics Simulation and Machine Learning Approach for Cryptic Site Prediction

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Computational Modeling of PROTAC Ternary Complexes as Ensembles Using SILCS-xTAC

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First-in-Class Potent, Dual HDAC6/Proteasome Inhibitors Lacking a Hydroxamic Acid Motif: Discovery of Novel Anti-Multiple Myeloma Agents

Chan, A. M., Lowe, B. D., Cottingham, A. L., Eker, D. V., Aytenfisu, A. H., Jones, J. W., MacKerell, A. D., Pearson, R. M., & Fletcher, S. (2025) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.5c00500 · Cited by 5 · see citing works

Comparative study of innovative computational methods for identifying cryptic pockets

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Mapping the distribution and affinities of ligand interaction sites on human serum albumin

Orr, A. A., Uwakweh, A.-O., Li, X., Karanji, A., Hoag, S. W., Deredge, D. J., & MacKerell, A. D. (2025) · Biophysical Journal · doi:10.1016/j.bpj.2025.03.016 · Cited by 2 · see citing works

Solvation Energetic Costs of Cognate Binding Site Formation

Ji, Y., Molino, V., Ramsey, S., & Kurtzman, T. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01432 · Cited by 1 · see citing works

How and Why Does Knowledge‐Based Biased Docking Improve Molecular Docking Performance?

Prieto, J. M., Lannot, J. O., Clemente, C. M., Modenutti, C. P., Turjanski, A. G., & Marti, M. A. (2026) · ChemMedChem · doi:10.1002/cmdc.202501058 · Cited by 1 · see citing works

Drude SILCS-Nucleic: harnessing explicit electronic polarization in targeting RNA and DNA for drug design

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Toward understanding the drivers of antibody-antigen binding

Khan, O., Kankkunen, M., Shaker, M., Chow, H., Moustakas, N., Allegra-Berger, S., Lazou, M., Vajda, S., & Joseph‐McCarthy, D. (2026) · Biophysical Journal · doi:10.1016/j.bpj.2026.02.008

Best Practices in Mixed-Solvent Molecular Dynamics and Solvent-Site-Biased Docking

Prieto, J. M., Estrada, J. A. D. C., Clemente, C. M., & Marti, M. A. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00082

Computational strategies for allosteric drug discovery: from cryptic pocket detection to rational design

Mukhopadhyay, S., & Chakrabarty, S. (2026) · Chemical Communications · doi:10.1039/d5cc06159h

Automated pharmacophore hypothesis generator for SILCS-Pharm: ph4gen

Nordquist, E. B., & MacKerell, A. D. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-026-00957-5

Identification and characterization of fragment binding sites for allosteric ligand design using the site identification by ligand competitive saturation hotspots approach (SILCS-Hotspots)

MacKerell, A. D., Jo, S., Lakkaraju, S. K., Lind, C., & Yu, W. (2020) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2020.129519 · Cited by 66 · see citing works

Application of site-identification by ligand competitive saturation in computer-aided drug design

Goel, H., Hazel, A. J., Yu, W., Jo, S., & MacKerell, A. D. (2021) · New Journal of Chemistry · doi:10.1039/d1nj04028f · Cited by 25 · see citing works

Optimization of the Site-Identification by Ligand Competitive Saturation (SILCS) as an Accurate and Reliable Technique in Lead Optimization

Ustach, V. D., Lakkaraju, S. K., Jo, S., Yu, W., Lin, F., Jiang, W., & MacKerell, A. D. (2019) · Biophysical Journal · doi:10.1016/j.bpj.2018.11.2581

Site identification by ligand competitive saturation (SILCS): Computational approach for the identification and optimization of ligands targeting proteins, RNA and other macromolecules

Alex, A., & MacKerell, E. D. (2013)

Computational Ligand-Binding Site Prediction

Nordquist, E. B., Horrigan, F. T., & MacKerell, A. D. (2026) · Advances in experimental medicine and biology · doi:10.1007/978-3-032-07511-6_7 · Cited by 1 · see citing works