Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability Domain<i>Included</i>

Berenger, Francois; Yamanishi, Yoshihiro

doi:10.1021/acs.jcim.9b01075

Cited by 4 publications

(4 citation statements)

References 70 publications

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“…To accelerate modeling and screening, the total number of molecules was capped to 65,000 (about 100 inactives per active molecule), using all actives but only a random partition from the inactive molecules. As machine-learning method, we used a prototype software developed in the lab using Kernel Density Estimate to rank-order molecules and at the same time obtain an applicability domain for the model ( Berenger and Yamanishi, 2020 ). The biweight kernel was used.…”

Section: Methodsmentioning

confidence: 99%

Ligand-based virtual-screening identified a novel CFTR ligand which improves the defective cell surface expression of misfolded ABC transporters

Taniguchi,

Berenger,

Doi

et al. 2024

Front. Pharmacol.

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Cystic fibrosis (CF) is a monogenetic disease caused by the mutation of CFTR, a cAMP-regulated Cl− channel expressing at the apical plasma membrane (PM) of epithelia. ∆F508-CFTR, the most common mutant in CF, fails to reach the PM due to its misfolding and premature degradation at the endoplasmic reticulum (ER). Recently, CFTR modulators have been developed to correct CFTR abnormalities, with some being used as therapeutic agents for CF treatment. One notable example is Trikafta, a triple combination of CFTR modulators (TEZ/ELX/IVA), which significantly enhances the functionality of ΔF508-CFTR on the PM. However, there’s room for improvement in its therapeutic effectiveness since TEZ/ELX/IVA doesn't fully stabilize ΔF508-CFTR on the PM. To discover new CFTR modulators, we conducted a virtual screening of approximately 4.3 million compounds based on the chemical structures of existing CFTR modulators. This effort led us to identify a novel CFTR ligand named FR3. Unlike clinically available CFTR modulators, FR3 appears to operate through a distinct mechanism of action. FR3 enhances the functional expression of ΔF508-CFTR on the apical PM in airway epithelial cell lines by stabilizing NBD1. Notably, FR3 counteracted the degradation of mature ΔF508-CFTR, which still occurs despite the presence of TEZ/ELX/IVA. Furthermore, FR3 corrected the defective PM expression of a misfolded ABCB1 mutant. Therefore, FR3 may be a potential lead compound for addressing diseases resulting from the misfolding of ABC transporters.

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Section: Methodsmentioning

confidence: 99%

Ligand-based virtual-screening identified a novel CFTR ligand which improves the defective cell surface expression of misfolded ABC transporters

Taniguchi,

Berenger,

Doi

et al. 2024

Front. Pharmacol.

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“…The computational teams used a variety of machine learning [47], docking [48,49] and hybrid approaches (Figure 2). In the group of machine learning based methods, approaches included: reinforcement learning, random forests [50], gradient boosting [51][52][53], kernelbased methods -e. g., Vanishing Ranking Kernels [54] and deep learning methods -e. g., self-normalizing-networks [55], LSTMs [56], CNNs [57][58][59][60][61], geometric deep learning, and graph neural networks [62][63][64]. Also stochastic-based methods -e. g., Naive Bayes Classifier [65] and Self-Consistent Regression [66] -were used.…”

Section: Virtual Screening Using Computational Methodsmentioning

confidence: 99%

A community effort in SARS‐CoV‐2 drug discovery

Schimunek,

Seidl,

Elez

et al. 2023

Molecular Informatics

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The COVID‐19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small‐molecule drugs that are widely available, including in low‐ and middle‐income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the “Billion molecules against COVID‐19 challenge”, to identify small‐molecule inhibitors against SARS‐CoV‐2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding‐, cleavage‐, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS‐CoV‐2 treatments.

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“…The computational teams used a variety of machine learning 33 , docking 34,35 and hybrid approaches (Figure 2). In the group of machine learning based methods, approaches included: reinforcement learning, random forests 36 , gradient boosting [37][38][39] , kernel-based methods-e.g., Vanishing Ranking Kernels 40 -and deep learning methods-e.g., self-normalizing-networks 41 , LSTMs 42 , CNNs [43][44][45][46][47] , geometric deep learning, and graph neural networks [48][49][50] . Also stochastic-based methods-e.g., Naive Bayes Classifier 51 and Self-Consistent Regression 52were used.…”

Section: Virtual Screening Using Computational Methodsmentioning

confidence: 99%

A community effort to discover small molecule SARS-CoV-2 inhibitors

Schimunek

Seidl

Elez

et al. 2023

Preprint

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The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of a community effort, the “Billion molecules against Covid-19 challenge”, to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 potentially active molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (Nsp12 domain), and (alpha) spike protein S. Overall, 27 potential inhibitors were experimentally confirmed by binding-, cleavage-, and/or viral suppression assays and are presented here. All results are freely available and can be taken further downstream without IP restrictions. Overall, we show the effectiveness of computational techniques, community efforts, and communication across research fields (i.e., protein expression and crystallography, in silico modeling, synthesis and biological assays) to accelerate the early phases of drug discovery.

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Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability DomainIncluded

Cited by 4 publications

References 70 publications

Ligand-based virtual-screening identified a novel CFTR ligand which improves the defective cell surface expression of misfolded ABC transporters

Ligand-based virtual-screening identified a novel CFTR ligand which improves the defective cell surface expression of misfolded ABC transporters

A community effort in SARS‐CoV‐2 drug discovery

A community effort to discover small molecule SARS-CoV-2 inhibitors

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