{"doi":"10.1002/9783527840748.ch7","title":"Recent Advances in Practical Quantum Mechanics and M<scp>ixed‐QM</scp>/<scp>MM</scp>‐Driven X‐Ray Crystallography and Cryogenic Electron Microscopy (<scp>Cryo‐EM</scp>) and Their Impact on Structure‐Based Drug Discovery","abstract":"With the steady, decades-long advance of faster, smarter computers, structure-based drug discovery (SBDD) and computer-aided drug design (CADD) have become indispensable tools for pharmaceutical research. Today, most pharmaceutical companies and research laboratories (academic as well as industrial) employ target-ligand structures to help inform the drug discovery effort. At the outset of any drug discovery campaign, if one or more experimental models are available, they are generally reviewed and scrutinized to determine what protein–ligand interactions are critical and what interactions could be better optimized in to build the next generation of compounds that will be safer and more effective than the last. At the same time, the tools that have become integral to pharmaceutical research – docking, scoring, structure prediction, dynamics, free energy modeling, etc. – are constantly evolving to increase speed and improve accuracy. Finally, recent advances in machine learning and artificial intelligence have led to advances in structure prediction based on the availability of pertinent models in the protein data bank (PDB). At the core of all this research and development is the availability of high-quality and highly accurate experimental models for these research efforts: garbage-in/garbage-out is a real and persistent problem as we look to next-generation cures and tools. X-ray crystallography and, to a lesser extent, nuclear magnetic resonance (NMR) have been the workhorses of structure solution. And with recent advances in cryogenic electron microscopy (Cryo-EM), we can now solve structures that previously would have been difficult to solve. But these experimental protocols all require significant computation themselves in the form of structure optimization (refinement) in the presence of experimental restraints (density). In the present work, we summarize recent advances in the field through the introduction of higher, more accurate levels of theory, and we discuss how these methods impact our understanding of the structure and how we can use this better understanding to inform both the SBDD process and the CADD tools at our disposal.","journal":null,"year":2024,"id":489711,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9531,"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":524215,"name":"Lance M. Westerhoff","orcid":"0000-0001-8092-6919","position":1,"is_corresponding":false},{"id":524831,"name":"Oleg Y. Borbulevych","orcid":null,"position":0,"is_corresponding":true}],"reference_count":74,"raw_metadata":null,"created_at":"2026-07-19T02:08:28.548530Z","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":[]}