From machine learning to deep learning: progress in machine intelligence for rational drug discovery

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

doi:10.1016/j.drudis.2017.08.010

Cited by 559 publications

(335 citation statements)

References 49 publications

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“…In recent years, a more data‐hungry ML algorithm, deep learning (DL), which has gained great success in a wide variety of applications, such as computer vision, speech recognition, computer games, and natural language processing, has also attracted considerable interest from computational chemists and medicinal chemists. Up to now, various reviews related to the applications of ML or DL in drug design and discovery have been published . Ain et al and Khamis et al summarized the advances of ML‐based SFs before 2015 in two comprehensive reviews about protein–ligand binding affinity prediction and SBVS, but DL has just begun to rise in the field of drug discovery in 2015 .…”

Section: Introductionmentioning

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

186

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: Introductionmentioning

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

186

180

View full text Add to dashboard Cite

show abstract

“…26,27 While biologists primarily use computers to understand their systems, the major goal of chemoinformaticians is to predict active (hit) molecules from large collections of candidate compounds, and then optimize their properties to achieve increased therapeutic activities and reduced toxicity risks (hit-to-lead). 28 Hence, chemoinformatics is mainly concerned with the predictive power of virtual screening and the efficiency of molecular design. Compared to biology, this has made the field more welcoming to mathematical abstraction, since explicit knowledge representation and mechanistic understanding are not indispensable requirements to endow correct predictions.…”

Section: Lessons From Chemoinformaticsmentioning

confidence: 99%

Formatting biological big data for modern machine learning in drug discovery

Duran‐Frigola

Fernández‐Torras

Bertoni

et al. 2018

WIREs Comput Mol Sci

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Biological data is accumulating at an unprecedented rate, escalating the role of data‐driven methods in computational drug discovery. This scenario is favored by recent advances in machine learning algorithms, which are optimized for huge datasets and consistently beat the predictive performance of previous art, rapidly approaching human expert reasoning. The urge to couple biological data to cutting‐edge machine learning has spurred developments in data integration and knowledge representation, especially in the form of heterogeneous, multiplex and semantically‐rich biological networks. Today, thanks to the propitious rise in knowledge embedding techniques, these large and complex biological networks can be converted to a vector format that suits the majority of machine learning implementations. Here, we explain why this can be particularly transformative for drug discovery where, for decades, customary chemoinformatics methods have employed vector descriptors of compound structures as the standard input of their prediction tasks. A common vector format to represent biology and chemistry may push biological information into most of the existing steps of the drug discovery pipeline, boosting the accuracy of predictions and uncovering connections between small molecules and other biological entities such as targets or diseases. This article is categorized under: Computer and Information Science > Databases and Expert Systems Computer and Information Science > Chemoinformatics

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“…Also, from a new paradigm in drug discovery is of the Poly pharmacology, which is the process off in ding new uses for existing approved drugs which focuses on multi-target drugs (MTDs), has potential application for drug repurposing, prediction of off-target toxicities and rational design of MTDs. The computational strategies have important role in it [76].…”

Section: Conclusion and Future Trendsmentioning

confidence: 99%

Drug Discovery - Yesterday and Tomorrow: The Common Approaches in Drug Design and Cancer

On¹

2018

CCLSJ

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The process of drug discovery has undergone radical changes and development over years. Traditionally, the drugs were discovered by employing chemistry and pharmacology-based cautious approach. When natural products were the most important source of drugs or drug precursors, but the conventional randomized drug research phenomenon was no longer effective at that time due to many negatives of these approaches like: high expenses of discovering new drugs, time-consuming and reduced success guarantee. Thus, with the development of the era, the concept of "Rational Drug Design" has enabled drug target identification and validation to be more specific. In addition, several novel technologies and approaches have been introducing economics, proteomics and other omics areas such as 3D QSAR, pharmacophore modeling and other, which playing a promising role in accelerating the pace of drug discovery process. Their view of the current research focuses on the importance of drug discovery in modern times and shows how old methods have been replaced and summarized. Some of examples of molecules are identified in addition to computational approaches used to discover it, specifically in the field of anticancer drug design

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From machine learning to deep learning: progress in machine intelligence for rational drug discovery

Cited by 559 publications

References 49 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

Formatting biological big data for modern machine learning in drug discovery

Drug Discovery - Yesterday and Tomorrow: The Common Approaches in Drug Design and Cancer

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