Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands

Durrant, Jacob D.; Carlson, Kathryn E.; Martin, Teresa A.; Offutt, Tavina L.; Mayne, Christopher G.; Katzenellenbogen, John A.; Amaro, Rommie E.

doi:10.1021/acs.jcim.5b00241

Cited by 33 publications

(22 citation statements)

References 55 publications

(133 reference statements)

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“…Further assessment on the DUD dataset showed that NNScore 2.0 did not perform as well as NNScore 1.0 on average, but outperformed AutoDock, AutoDock Vina and Glide HTVS . In addition, NNScore was also involved in the discovery of inhibitors towards several important targets, which further testified its practicality …”

Section: Traditional Machine Learning Methods In Scoring Functionsmentioning

confidence: 99%

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Shen

Ding

Wang

et al. 2019

WIREs Comput Mol Sci

175

180

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Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy highly depends on the reliability of scoring functions (SFs). With the rapid development of machine learning (ML) techniques, ML-based SFs have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and virtual screening, and most of them have shown significantly better performance than a wide range of classical SFs. Emergence of more data-hungry deep learning (DL) approaches in recent years further fascinates the exploitation of more accurate SFs. Here, we summarize the progress of traditional ML-based SFs in the last few years and provide insights into recently developed DL-based SFs. We believe that the continuous improvement in ML-based SFs can surely guide the early-stage drug design and accelerate the discovery of new drugs. This article is categorized under:Computer and Information Science > Chemoinformaticsdeep learning, machine learning, molecular docking, scoring function, structure-based drug design | INTRODUCTIONTraditional drug discovery largely relies on the application of high-throughput screening, an experimental technique with acceptable performance but high cost and low efficiency. 1 With the rapid development of computational chemistry and computer technology, computer-aided drug design (CADD) has gradually emerged as a powerful technique in the design and development of new drug candidates in the past three decades. 2 Virtual screening (VS), an important branch of CADD, can enrich potential actives from large virtual compound libraries through in silico methods rather than real experiments, which can not only accelerate the process of drug discovery but also greatly reduce the time and resource cost. 3-5 Depending on whether the three-dimensional (3D) structure of a target is used or not, VS approaches can be classified into two major categories: ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS). 6 LBVS aims to discover active molecules through the models developed based on a set of known ligands of a target of interest, which may limit its capability to find novel chemotypes. Compared with LBVS, SBVS is considered to be a better choice to discover novel active compounds if the 3D structure of a given target is available. 7 Chao Shen and Junjie Ding are equivalent first authors.

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Section: Traditional Machine Learning Methods In Scoring Functionsmentioning

confidence: 99%

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Shen

Ding

Wang

et al. 2019

WIREs Comput Mol Sci

175

180

View full text Add to dashboard Cite

show abstract

“…In addition to evaluating the SFs on the entire test set, their performance on certain test subsets would be very informative. For instance, on the subset of complexes bound by molecules dissimilar to any training set molecule, as SFs performing well here should discover a higher proportion of novel compounds [5,8,48]. Another example is the subset of complexes with most potent actives/binders (e.g.…”

Section: Selecting a Scoring Function Based On Your Own Evaluationmentioning

confidence: 99%

“…Molecular docking is arguably the most common tool for SBVS. This type of technology has been employed to discover active molecules with novel chemical scaffolds in a fast and cost-effective manner [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Selecting machine-learning scoring functions for structure-based virtual screening

Ballester

2019

Drug Discovery Today: Technologies

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Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

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“…where ML SFs for SBVS directly discovered relatively potent leads, e.g. 460 nM to 20 µM (Durrant et al, 2015) or 359 nM to 18.2 µM (Sun et al, 2016). The generic version of RF-Score-VS (RF-Score-VS_G) not only improved the potency of the retrieved actives, it also achieved an average hit rate that is three times higher than that of SMINA.…”

Section: Discussionmentioning

confidence: 99%

The impact of compound library size on the performance of scoring functions for structure-based virtual screening

Fresnais

Ballester

2020

Preprint

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Motivation: Larger training datasets have been shown to improve the accuracy of Machine Learning (ML)-based Scoring functions (SFs) for Structure-Based Virtual Screening (SBVS). In addition, massive test sets for SBVS, known as ultra-large compound libraries, have been demonstrated to enable the fast discovery of selective drug leads with at least nanomolar potency. This proof-of-concept was carried out on two targets using a single docking tool along with its SF. It is thus unclear whether this high level of performance would generalise to other targets, docking tools and SFs. Results:We found that screening a larger compound library results in more potent actives being identified in all six additional targets using a different docking tool along with its classical SF. Furthermore, we established that a way to improve the potency of the retrieved molecules further is to rank them with more accurate ML-based SFs (we found this true in four of the six targets). A three-fold increase in average hit rate across targets was also achieved by the ML-based SFs. Lastly, we observed that classical and ML-based SFs often find different actives, which supports using both types of SFs on those targets.

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Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands

Cited by 33 publications

References 55 publications

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Selecting machine-learning scoring functions for structure-based virtual screening

The impact of compound library size on the performance of scoring functions for structure-based virtual screening

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