دورية أكاديمية
Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules.
| العنوان: | Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules. |
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| المؤلفون: | Pandey P; Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St., HSF II-633, Baltimore, Maryland 21201, United States., MacKerell AD Jr; Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St., HSF II-633, Baltimore, Maryland 21201, United States. |
| المصدر: | Journal of chemical information and modeling [J Chem Inf Model] 2023 Sep 25; Vol. 63 (18), pp. 5903-5915. Date of Electronic Publication: 2023 Sep 08. |
| نوع المنشور: | Journal Article; Research Support, N.I.H., Extramural |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Washington, D.C. : American Chemical Society, c2005- |
| مواضيع طبية MeSH: | Artificial Intelligence* , Chemistry, Pharmaceutical*, Ligands ; Permeability ; Cell Membrane Permeability |
| مستخلص: | Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R 2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design. |
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| معلومات مُعتمدة: | R35 GM131710 United States GM NIGMS NIH HHS |
| المشرفين على المادة: | 0 (Ligands) |
| تواريخ الأحداث: | Date Created: 20230908 Date Completed: 20230926 Latest Revision: 20231029 |
| رمز التحديث: | 20240628 |
| مُعرف محوري في PubMed: | PMC10603762 |
| DOI: | 10.1021/acs.jcim.3c00514 |
| PMID: | 37682640 |
| قاعدة البيانات: | MEDLINE |
Find this issue from the American Chemical Society | تدمد: | 1549-960X |
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| DOI: | 10.1021/acs.jcim.3c00514 |