Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery

By Benjamin P. Brown, Oanh Vu, Alexander R. Geanes, Sandeepkumar Kothiwale, Mariusz Butkiewicz, E. W. Lowe, Ralf Mueller, Richard Pape, Jeffrey L. Mendenhall, Jens Meiler. 2022

22 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Anima Anandkumar
2. Weili Nie
3. Arash Vahdat
4. Thomas F. Miller
5. Zhuoran Qiao

In Materials Chemistry

1. Anima Anandkumar
2. Weili Nie
3. Arash Vahdat
4. Thomas F. Miller
5. Zhuoran Qiao

In Molecular Biology

1. Anima Anandkumar
2. Weili Nie
3. Arash Vahdat
4. Thomas F. Miller
5. Zhuoran Qiao

Citations in the corpus

• State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022, neutral): “[38] Tom Brown et al. “Language models are few-shot learners”. In: Advances in neural information processing systems 33 (2020), pp. 1877–1901. [39] Aditya Ramesh et al. “Hierarchical text-conditional image generation with clip latents”. In: arXiv preprint arXiv:2204.06125 (2022). [40] Chitwan Sahari”
• Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models (2022, methodology): “0 to enable a fully flexible receptor; the ligand coordinates are initialized using the aligned-ground-truth conformation as obtained by TM-Align [49], with randomized torsion angles using the BCL [69] library as described in the standard protocol.”