{"doi":"10.1002/9783527840748.ch5","title":"<scp>Site‐Identification</scp>by Ligand Competitive Saturation as a Paradigm of Co‐solvent<scp>MD</scp>Methods","abstract":"Co-solvent molecular dynamics (MD) methods represent important tools in the collection of computer-aided drug design (CADD) methods used in drug design and discovery. These methods apply physics-based models to predict the distribution of regions on a macromolecular target that are suitable or unsuitable for interactions with specific classes of functional groups that are represented by the co-solvents within the MD simulations. Notable is the use of explicit water in the MD simulations along with the co-solvents such that the resulting functional group affinity patterns represent free energies, which account for co-solvent and target macromolecule desolvation penalties, target flexibility, as well as co-solvent-target interactions. Of the co-solvent MD methods, Site Identification by Ligand Competitive Saturation (SILCS) involves the simultaneous use of diverse small solutes in aqueous solution to obtain functional group affinity patterns of the target macromolecule. SILCS uses a combined Grand Canonical Monte Carlo (GCMC)-MD approach to facilitate water and solute sampling of the full 3D space of the target macromolecule, including interior cavities and deep-binding regions, as well as conformational flexibility of the target macromolecule. Normalization and Boltzmann transformation of the functional group affinity patterns are used to produce functional group free energy maps (FragMaps) that may be used in many aspects of CADD. Beyond small-molecule-focused CADD, the SILCS approach may be used for the calculation of ligand passive permeabilities as well as to facilitate the formulation of biologics such as monoclonal antibodies. The present chapter gives an overview of co-solvent MD simulation methods, followed by a more detailed presentation of various aspects of the SILCS technology along with case studies demonstrating its application for the proteins bovine serum albumin (BSA) and pembrolizumab.","journal":null,"year":2024,"id":487737,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9508,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":309315,"name":"Alexander D. MacKerell","orcid":"0000-0001-8287-6804","position":1,"is_corresponding":false},{"id":270827,"name":"Asuka A. Orr","orcid":"0000-0003-4628-526X","position":0,"is_corresponding":true}],"reference_count":178,"raw_metadata":null,"created_at":"2026-07-19T02:08:10.215754Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}