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

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

doi:10.3389/fphar.2022.833099

Cited by 12 publications

(30 citation statements)

References 68 publications

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“…We have previously suggested that Y512 serves to physically block a bound vanilloid molecule from exerting the binding pocket (16), in agreement with structural observations (15). Molecular docking of RTX to TRPV1 structures in the closed (C1 and C2) and open (O1) states using RosettaLigand (17, 18) suggested reductions in Rosetta binding energy by this point mutation, in supportive of weakening of RTX binding (Fig. S4).…”

Section: Resultsmentioning

confidence: 94%

“…RosettaLigand (17) was used to dock RTX to wild-type and Y511A mutant structures for each state. We generated 2,000 RTX conformers for the docking process using BioChemical Library (BCL) (18). The detail of the docking algorithm has been described elsewhere (17) (see Appendix for Rosetta docking scripts and command lines).…”

Section: Rosetta Modeling Of Rtx Binding To Mutant Trpv1 Channelsmentioning

confidence: 99%

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TRPV1 Opening is Stabilized Equally by Its Four Subunits

Nguyen

et al. 2023

Preprint

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Capsaicin receptor TRPV1 is a nociceptor for vanilloid molecules such as capsaicin and resiniferatoxin (RTX). Even though cryo-EM structures of TRPV1 in complex with these molecules are available, how their binding energetically favors the open conformation is not known. Here we report an approach to control the number of bound RTX molecules (0-to-4) in functional mouse TRPV1. The approach allowed direct measurements of each of the intermediate open states under equilibrium conditions at both macroscopic and single-molecule levels. We found that RTX binding to each of the four subunits contributes virtually the same activation energy, which we estimated to be 1.86 kcal/mol and found to arise predominately from destabilizing the closed conformation. We further showed that sequential bindings of RTX increase open probability without altering single-channel conductance, confirming that there is likely a single open-pore conformation for TRPV1 activated by RTX.

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Section: Resultsmentioning

confidence: 94%

Section: Rosetta Modeling Of Rtx Binding To Mutant Trpv1 Channelsmentioning

confidence: 99%

TRPV1 Opening is Stabilized Equally by Its Four Subunits

Nguyen

et al. 2023

Preprint

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“…Standardized 3D coordinates are generated using Corina [22], version 4.3. Molecules are further filtered with atom type validity and duplicates with the BioChemical Library (BCL) [23].…”

Section: • Datasetsmentioning

confidence: 99%

Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors

Liu

Moretti

Wang

et al. 2023

Preprint

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In recent years several applications of graph neural networks (GNNs) to molecular tasks have emerged. Whether GNNs outperform the traditional descriptor-based methods in the quantitative structure activity relationship (QSAR) modeling in early computer-aided drug discovery (CADD) remains an open question. This paper introduces a simple yet effective strategy to boost the predictive power of QSAR deep learning models. The strategy proposes to train GNNs together with traditional descriptors, combining the strengths of both methods. The enhanced model consistently outperforms vanilla descriptors or GNN methods on nine well-curated high throughput screening datasets over diverse therapeutic targets.

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“…Conformations are generated using Corina (Gasteiger, Rudolph, and Sadowski 1990), version 4.3. Molecules are further filtered with validity, duplicates with BioChemical Library (BCL) (Brown et al 2022).…”

Section: Supplementary Materialsmentioning

confidence: 99%

Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery

Liu

Wang

et al. 2022

Preprint

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In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular-Kernel Graph Neural Network (MolKGNN) for molecular representation learning, which features SE(3)-/conformation invariance, and interpretability. For our MolKGNN, we first design a molecular graph convolution to capture the chemical pattern by comparing the atom's similarity with the learnable molecular kernels. Furthermore, we propagate the similarity score to capture the higher-order chemical pattern. To assess the method, we conduct a comprehensive evaluation with nine well-curated datasets spanning numerous important drug targets that feature realistic high class imbalance and it demonstrates the superiority of MolKGNN over other GNNs in CADD. Meanwhile, the learned kernels identify patterns that agree with domain knowledge, confirming the pragmatic interpretability of this approach. Our codes are publicly available at https://github.com/meilerlab/MolKGNN.

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

Cited by 12 publications

References 68 publications

TRPV1 Opening is Stabilized Equally by Its Four Subunits

TRPV1 Opening is Stabilized Equally by Its Four Subunits

Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors

Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery

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