%0 Journal Article %1 Brown2022-yz %A Brown, Benjamin P %A Vu, Oanh %A Geanes, Alexander R %A Kothiwale, Sandeepkumar %A Butkiewicz, Mariusz %A Lowe, Jr, Edward W %A Mueller, Ralf %A Pape, Richard %A Mendenhall, Jeffrey %A Meiler, Jens %D 2022 %I Frontiers Media SA %J Front. Pharmacol. %K topic_lifescience BCL; QSAR; biochemical cheminformatics; deep design; discovery; drug library; network; neural open-source %P 833099 %T Introduction to the BioChemical Library (BCL): An application-based open-source toolkit for integrated cheminformatics and machine learning in computer-aided drug discovery %V 13 %X The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines.

@article{Brown2022-yz, abstract = {The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines.}, added-at = {2024-09-10T11:54:51.000+0200}, author = {Brown, Benjamin P and Vu, Oanh and Geanes, Alexander R and Kothiwale, Sandeepkumar and Butkiewicz, Mariusz and {Lowe, Jr}, Edward W and Mueller, Ralf and Pape, Richard and Mendenhall, Jeffrey and Meiler, Jens}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/281800fcdc3cdcf588ad0c8ebb4c4eb5d/scadsfct}, copyright = {https://creativecommons.org/licenses/by/4.0/}, interhash = {17fb8aeec2144572fdbf94ee72a995c9}, intrahash = {81800fcdc3cdcf588ad0c8ebb4c4eb5d}, journal = {Front. Pharmacol.}, keywords = {topic_lifescience BCL; QSAR; biochemical cheminformatics; deep design; discovery; drug library; network; neural open-source}, language = {en}, month = feb, pages = 833099, publisher = {Frontiers Media SA}, timestamp = {2024-11-28T17:41:23.000+0100}, title = {Introduction to the {BioChemical} Library ({BCL)}: An application-based open-source toolkit for integrated cheminformatics and machine learning in computer-aided drug discovery}, volume = 13, year = 2022 }