Development of a Protein-Ligand Extended Connectivity (PLEC) Fingerprint and Its Application for Binding Affinity Predictions.

Wójcikowski, Maciej; Kukiełka, Michał; Stepniewska-Dziubinska, Marta M.; Siedlecki, Paweł

doi:10.26434/chemrxiv.5928406.v1

Cited by 30 publications

(64 citation statements)

References 19 publications

(25 reference statements)

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“…Our differential geometry-based model is in third place with R p = 0.774. The fourth place in the ranking table is PLEC-nn, 151 a deep learning network model. -2016 4mrw, 4mrz, 4msn, 5c1w, 4msc, 3cyx a) c) Figure 11: The performances on different evaluation metrics of various scoring functions on the CASF-2013 benchmark.…”

Section: Ivb Performance and Discussionmentioning

confidence: 99%

“…a) scoring power ranked by Pearson correlation coefficient, b) ranking power assessed by the high-level success measurement, and c) docking power measured by the rate of successfully identifying the "native" pose from 100 poses for each ligand. Our developed models, namely TopBP, 21 EIC-Score, 100 and AGL-Score 96 are colored in orange, and other scoring functions 86,87,100,143,151 are colored in teal. Figure 12: The Pearson correlation coefficient of various scoring functions on CASF-2016.…”

Section: Ivb Performance and Discussionmentioning

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: Ivb Performance and Discussionmentioning

confidence: 99%

Section: Ivb Performance and Discussionmentioning

confidence: 99%

A review of mathematical representations of biomolecular data

Nguyen

Cang

Wei

2020

Phys. Chem. Chem. Phys.

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“…Machine learning methods at varying levels of sophistication have long been considered in the context of structure-based virtual screening [39,31,32,[40][41][42][43][44][45][46]29,[47][48][49][50][51][52][53][54]. The vast majority of such studies sought to train a regression model that would recapitulate the binding affinities of known complexes, and thus provide a natural and intuitive replacement for traditional scoring functions [31,32,[41][42][43][44][45][46]29,47,49,[51][52][53][54]. The downside of such a strategy, however, is that the resulting models are not ever exposed to any inactive complexes in the course of training: this is especially important in the context of docked complexes arising from virtual screening, where most compounds in the library are presumably inactive.…”

Section: Developing a Challenging Training Setmentioning

confidence: 99%

“…To confirm that this decoy-generation strategy indeed led to a challenging classification problem, we applied some of the top reported scoring functions in the literature to distinguish between active and decoy complexes in the D-COID set. For all eight methods tested (nnscore [32], RF-Score v1 [31], RF-Score v2 [44], RF-Score v3 [29], PLEClinear [53], PLECnn [53], PLECrf [53], and RF-Score-VS…”

Section: Developing a Challenging Training Setmentioning

confidence: 99%

“…Each of these complexes (both actives and decoys) were then subjected to energy minimization using the Rosetta: as noted earlier, vScreenML should only be applied in the context of Rosetta-minimized structures. Along with vScreenML, we used eight other machine learning scoring functions were then used to rank the docked-and-minimized models: nnscore [32], PLECnn [53], PLECrf [53], PLEClinear [53], RF-Score v1 [31], RF-Score v2 [44], RF-Score v3 [29] and RF-Score-VS [49]. We additionally included the (default) Rosetta energy function in this benchmark [61].…”

Section: Benchmarking Vscreenml Using Independent Test Setsmentioning

confidence: 99%

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Machine learning classification can reduce false positives in structure-based virtual screening

Adeshina

Deeds

Karanicolas

2020

Preprint

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With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter.Modern virtual screening methods are still, however, plagued with high false positive rates: typically, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays.We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because none of the studies reporting new scoring methods have validated their model prospectively within the same study. Here, we report a new strategy for building a training dataset (D-COID) that aims to generate highly-compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework of gradient-boosted decision trees. In retrospective benchmarks, our new classifier shows outstanding performance relative to other scoring functions. We additionally evaluate the classifier in a prospective context, by screening for new acetylcholinesterase inhibitors. Remarkably, we find that nearly all compounds selected by vScreenML show detectable activity at 50 µM, with 10 of 23 providing greater than 50% inhibition at this concentration. Without any medicinal chemistry optimization, the most potent hit from this initial screen has an IC50 of 280 nM, corresponding to a Ki value of 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.

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Machine‐learning scoring functions for structure‐based virtual screening

Sze

Lü

et al. 2020

WIREs Comput Mol Sci

122

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Molecular docking predicts whether and how small molecules bind to a macromolecular target using a suitable 3D structure. Scoring functions for structure-based virtual screening primarily aim at discovering which molecules bind to the considered target when these form part of a library with a much higher proportion of non-binders. Classical scoring functions are essentially models building a linear mapping between the features describing a proteinligand complex and its binding label. Machine learning, a major subfield of artificial intelligence, can also be used to build fast supervised learning models for this task. In this review, we analyzed such machine-learning scoring functions for structure-based virtual screening in the period 2015-2019. We have discussed what the shortcomings of current benchmarks really mean and what valid alternatives have been employed. The latter retrospective studies observed that machine-learning scoring functions were substantially more accurate, in terms of higher hit rates and potencies, than the classical scoring functions they were compared to. Several of these machine-learning scoring functions were also employed in prospective studies, in which mid-nanomolar binders with novel chemical structures were directly discovered without any potency optimization. We have thus highlighted the codes and webservers that are available to build or apply machine-learning scoring functions to prospective structure-based virtual screening studies. A discussion of prospects for future work completes this review.

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Development of a Protein-Ligand Extended Connectivity (PLEC) Fingerprint and Its Application for Binding Affinity Predictions.

Cited by 30 publications

References 19 publications

A review of mathematical representations of biomolecular data

A review of mathematical representations of biomolecular data

Machine learning classification can reduce false positives in structure-based virtual screening

Machine‐learning scoring functions for structure‐based virtual screening

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