06d3b14e9f01c5e48491f171f53f624c pmc_11118195.pdf b0be459537b5bad387d4c1e08aad24ca408665fc pmc_11118195.pdf cca1b7fc6d703618d81838c59a0dccc222e998daa4f36e384ab6512eec474eab pmc_11118195.pdf Title: Virtual Screening of Small Molecules Targeting BCL2 with Machine Learning, Molecular Docking, and MD Simulation Subject: This study aimed to identify potential BCL-2 small molecule inhibitors using deep neural networks (DNN) and random forest (RF), algorithms as well as molecular docking and molecular dynamics (MD) simulations to screen a library of small molecules. The RF model classified 61% (2355/3867) of molecules as ‘Active’. Further analysis through molecular docking with Vina identified CHEMBL3940231, CHEMBL3938023, and CHEMBL3947358 as top-scored small molecules with docking scores of -11, -10.9, and 10.8 kcal/mol, respectively. MD simulations validated these compounds’ stability and binding affinity to the BCL2 protein. Keywords: small molecules; virtual screening; BCL2; cancer therapeutics Author: Abtin Tondar, Sergio Sánchez-Herrero, Asim Kumar Bepari, Amir Bahmani, Laura Calvet Liñán and David Hervás-Marín Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Fri May 10 08:28:55 2024 CEST ModDate: Fri May 10 08:41:27 2024 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 18 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 4941575 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- SSNIUI+VnURWPalladioL-Bold Type 1 Custom yes yes yes 10 0 IQCBPK+URWPalladioL-Bold Type 1 Custom yes yes yes 16 0 ZEWWCV+URWPalladioL-Roma Type 1 Custom yes yes yes 22 0 FTJPEG+URWPalladioL-Ital Type 1 Custom yes yes yes 27 0 JDUPKS+CMSY10 Type 1 Builtin yes yes yes 32 0 OYGWHX+VnURWPalladioL Type 1 Custom yes yes yes 37 0 IVVSWS+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 55 0 BCDEEE+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 94 0 BCDFEE+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 98 0 BCDGEE+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 102 0 BCDHEE+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 106 0 BCDEEE+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 125 0 BCDFEE+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 129 0 BCDGEE+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 133 0 BCDHEE+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 137 0 BCDEEE+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 157 0 BCDFEE+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 161 0 BCDGEE+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 165 0 BCDHEE+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 169 0 JCHMGB+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 191 0 JCHMHC+PalatinoLinotype TrueType WinAnsi yes yes no 194 0 JCHMHE+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 197 0 BCDEEE+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 210 0 BCDFEE+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 214 0 BCDGEE+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 218 0 BCDEEE+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 231 0 BCDFEE+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 235 0 BCDGEE+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 239 0 BCDHEE+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 243 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-01-31 02:46:13 CET RepresentationInformation: pmc_11118195.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-12-12 02:29:41 CET Size: 4941575 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 484 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Methods Destination: section.2 Children: Item: Title: Activity Dataset Destination: subsection.2.1 Item: Title: Ligand Library for Screening Destination: subsection.2.2 Item: Title: Deep Neural Network (DNN) Machine Learning Models Destination: subsection.2.3 Item: Title: Random Forest (RF) Machine Learning Model Destination: subsection.2.4 Item: Title: Homology Modeling of BCL2 Destination: subsection.2.5 Item: Title: AutoDock Vina (Vina) Molecular Docking Destination: subsection.2.6 Item: Title: Molecular Dynamics (MD) Simulations Destination: subsection.2.7 Item: Title: Results Destination: section.3 Children: Item: Title: Activity Dataset Destination: subsection.3.1 Item: Title: Ligand Library for Screening Destination: subsection.3.2 Item: Title: Deep Neural Network (DNN) Machine Learning Models Destination: subsection.3.3 Item: Title: Random Forest (RF) Machine Learning Model Destination: subsection.3.4 Item: Title: Screening Small Molecules Using the RF Model Destination: subsection.3.5 Item: Title: Vina Molecular Docking Destination: subsection.3.6 Item: Title: Molecular Docking Simulations Destination: subsection.3.7 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: appendix.A. Info: Title: Virtual Screening of Small Molecules Targeting BCL2 with Machine Learning, Molecular Docking, and MD Simulation Author: Abtin Tondar, Sergio Sánchez-Herrero, Asim Kumar Bepari, Amir Bahmani, Laura Calvet Liñán and David Hervás-Marín Subject: This study aimed to identify potential BCL-2 small molecule inhibitors using deep neural networks (DNN) and random forest (RF), algorithms as well as molecular docking and molecular dynamics (MD) simulations to screen a library of small molecules. The RF model classified 61% (2355/3867) of molecules as ‘Active’. Further analysis through molecular docking with Vina identified CHEMBL3940231, CHEMBL3938023, and CHEMBL3947358 as top-scored small molecules with docking scores of -11, -10.9, and 10.8 kcal/mol, respectively. MD simulations validated these compounds’ stability and binding affinity to the BCL2 protein. 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