TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data

Abstract

Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, and easy-to-share pipelines. While documentation for such tools is available, there are only a few freely accessible examples that teach the underlying concepts focused on CADD, especially addressing users new to the field. Here, we present TeachOpenCADD, a teaching platform developed by students for students, using open source compound and protein data as well as basic and CADD-related Python packages. We provide interactive Jupyter notebooks for central CADD topics, integrating theoretical background and practical code. TeachOpenCADD is freely available on GitHub: https://github.com/volkamerlab/TeachOpenCADD .

Towards reproducible computational drug discovery

“…This resulted in the development of Python-based tutorials pertaining to the virtual screening workflow to identify malarial drugs [273,274]. Furthermore, the recently launched TeachOpenCADD platform [275] complements the already available resources by providing students and researchers who are new to computational drug discovery and/or programming with step-by-step talktorials that cover both ligand-and structure-based approaches using Python-based open source packages in interactive Jupyter notebooks [276].…”

QSAR-Based Virtual Screening of Natural Products Database for Identification of Potent Antimalarial Hits

“…Three criteria were used for selection of virtual hits: (i) predicted pEC 50 against P. falciparum 3D7 should be ≥6; (ii) probability of activity (p) against P. falciparum W2 should be ≥0.6 (p > 60%) and (iii) logP filters were also added to predict good lipophilicity with logP < 3 using XLogP [ 27 ]. For prioritization of structurally diverse compounds, molecules predicted to be active by the virtual screening were clustered through the Butina method [ 28 ] implemented in Python 3.6 and using the workflow proposed by Sydow and colleagues [ 29 ], which groups compounds based on Tanimoto similarity and picks a set of diverse compounds from these groups. Finally, the selected virtual hits were purchased and submitted to in vitro experimental evaluation.…”

TeachOpenCADD 2021: Open Source and FAIR Python Pipelines to Assist in Structural Bioinformatics and Cheminformatics Research

“…In 2019, we launched the teaching platform TeachOpenCADD [4] on GitHub to help face these challenges (https://github.com/volkamerlab/teachopencadd). TeachOpenCADD teaches by example how to build pipelines with open source resources used in the fields of cheminformatics and structural bioinformatics to answer central questions in computer-aided drug design (CADD).…”

“…The initial stack of talktorials T001-T010 covers common CADD tasks involving webserver queries, cheminformatics, and structural bioinformatics [4]. We show how to fetch chemical and structural data from the ChEMBL [6] and PDB [7,8] online databases and how to encode, filter, cluster, and screen such datasets to find novel drug candidates and off-targets [4] (Figure 1, T001-T010).…”

“…The initial stack of talktorials T001-T010 covers common CADD tasks involving webserver queries, cheminformatics, and structural bioinformatics [4]. We show how to fetch chemical and structural data from the ChEMBL [6] and PDB [7,8] online databases and how to encode, filter, cluster, and screen such datasets to find novel drug candidates and off-targets [4] (Figure 1, T001-T010). The TeachOpenCADD platform enjoys wide usage in the community, as exemplified by frequently posted GitHub issues, about 13, 000 article views [9], over 250 GitHub repository stars and about 90 repository forks (as of 2021-10-14) [10] as well as teaching feedback [11].…”