Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest

Wang, Cheng; Zhang, Yingkai

doi:10.1002/jcc.24667

Cited by 255 publications

(291 citation statements)

References 75 publications

(118 reference statements)

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“…Therefore, they are included in our experiments. Our developed models, namely TopBP, 21 EIC-Score, 100 and AGL-Score 96 are colored in orange, and other scoring functions 10,24,37,84,85,100,143 are colored in teal.…”

Section: Iva Hyperparameter Optimizationmentioning

confidence: 99%

“…The teal bars express the performances of other models Refs. 87,143 set in that database. Besides these experimental structures, we generate the non-binder structures for each target protein by using Autodock Vina.…”

Section: Ivb4 Screening Powermentioning

confidence: 99%

“…Figure 13 plots the performance of the AGL-Score along with numerous scoring functions reported in the literature. 87,143 It is an encouragement to see our AGL-Score achieves the top performance on enrichment factor (EF) and success rate at the top 1% level in the CASF-2013 benchmark. The EF of the AGL-Score is 25.6 followed by ∆ vina RF 20 (EF=20.9) 143 and GlideScore-SP (EF=19.5).…”

Section: Ivb4 Screening Powermentioning

confidence: 99%

“…87,143 It is an encouragement to see our AGL-Score achieves the top performance on enrichment factor (EF) and success rate at the top 1% level in the CASF-2013 benchmark. The EF of the AGL-Score is 25.6 followed by ∆ vina RF 20 (EF=20.9) 143 and GlideScore-SP (EF=19.5). 87 Moreover, the success rate of our graph-based model is 68% followed by ∆ vina RF 20 and GlideScore-SP that both attain 60%.…”

Section: Ivb4 Screening Powermentioning

confidence: 99%

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A review of mathematical representations of biomolecular data

Nguyen

Cang

Wei

2020

Phys. Chem. Chem. Phys.

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Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges. However, the intricate structural complexity and high ML dimensionality of biomolecular datasets obstruct the efficient application of ML algorithms in the field. In addition to data and algorithm, an efficient ML machinery for biomolecular predictions must include structural representation as an indispensable component. Mathematical representations that simplify the biomolecular structural complexity and reduce ML dimensionality have emerged as a prime winner in D3R Grand Challenges. This review is devoted to the recent advances in developing low-dimensional and scalable mathematical representations of biomolecules in our laboratory. We discuss three classes of mathematical approaches, including algebraic topology, differential geometry, and graph theory. We elucidate how the physical and biological challenges have guided the evolution and development of these mathematical apparatuses for massive and diverse biomolecular data. We focus the performance analysis on the protein-ligand binding predictions in this review although these methods have had tremendous success in many other applications, such as protein classification, virtual screening, and the predictions of solubility, solvation free energy, toxicity, partition coefficient, protein folding stability changes upon mutation, etc.

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Section: Iva Hyperparameter Optimizationmentioning

confidence: 99%

Section: Ivb4 Screening Powermentioning

confidence: 99%

Section: Ivb4 Screening Powermentioning

confidence: 99%

Section: Ivb4 Screening Powermentioning

confidence: 99%

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A review of mathematical representations of biomolecular data

Nguyen

Cang

Wei

2020

Phys. Chem. Chem. Phys.

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“…Instead they are trained on large datasets to model the relationships between affinity and various descriptors. According to the latest literature data, the machine learning scoring functions can estimate the binding energy most successfully.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Classification Models to Improve the Docking‐based Screening: A Case of PI3K‐Tankyrase Inhibitors

Berishvili

Voronkov

Radchenko

et al. 2018

Molecular Informatics

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One of the major challenges in the current drug discovery is the improvement of the docking-based virtual screening performance. It is especially important in the rational design of compounds with desired polypharmacology or selectivity profiles. To address this problem, we present a methodology for the development of target-specific scoring functions possessing high screening power. These scoring functions were built using the machine learning methods for the dual target inhibitors of PI3Kα and tankyrase, promising targets for colorectal cancer therapy. The Deep Neural Network models achieve the external test AUC ROC values of 0.96 and 0.93 for the random split and 0.90 and 0.84 for the time-based split of the PI3Kα and tankyrase inhibitors, respectively. In addition, the impact of the training set size and the actives/decoys ratio on the model quality was assessed. The study demonstrates that the optimized scoring functions could significantly improve the docking screening power for each individual target. This is very useful in the design of multitarget or selective drugs wherein the screening filters are applied in sequence.

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Docking, Scoring, and Virtual Screening in Drug Discovery

Spyrakis

Sarkar

Kellogg

2021

Burger's Medicinal Chemistry and Drug Discovery

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Since its introduction about four decades ago, docking and scoring are now the very heart of structure‐based drug design. The past 10–20 years have seen a plethora of docking and scoring tools successfully integrated into drug discovery pipelines. Interestingly, while artificial intelligence is now receiving significant attention in the computational modeling arena, docking and scoring have long utilized these techniques. This comprehensive summary highlights some of the most significant achievements of docking and scoring, reviews the current status, provides descriptions of many of the tools available, comments on some of the outstanding challenges facing the paradigm, and offers perspectives and advice on best practices for users. While significant development is still needed in docking of flexible molecules and accurate Gibb's free energy predictions, docking and scoring are very useful when handled by experienced practitioners, but less so if treated as a “black box.” Lastly, we present a hypothetical case so beginners may appreciate the nuances of setting up a docking study. Focus in docking is now shifting toward parallel applications, i.e. protein–protein, protein–oligosaccharide, protein–DNA, or protein–RNA docking and polypharmacology. In summary, this article is intended to elucidate the nuances of the subject, while providing guidelines for practical implementation of effective workflows in drug discovery and structural biology.

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Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest

Cited by 255 publications

References 75 publications

A review of mathematical representations of biomolecular data

A review of mathematical representations of biomolecular data

Machine Learning Classification Models to Improve the Docking‐based Screening: A Case of PI3K‐Tankyrase Inhibitors

Docking, Scoring, and Virtual Screening in Drug Discovery

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