Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

Jing, Yankang; Bian, Yuemin; Hu, Ziheng; Wang, Lirong; Xie, Xiang‐Qun

doi:10.1208/s12248-018-0210-0

Cited by 290 publications

(199 citation statements)

References 71 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

195

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

195

180

View full text Add to dashboard Cite

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“…An ANN model can contain multiple hidden layers, but additional hidden layers greatly increase computational time and complexity of the algorithm. 96 Within each hidden layer, a series of mathematical processes occurs, which weights the information of each input based on its importance to predicting the outputs. This process is defined using a loss function, which evaluates model performance, essentially working to minimize the "error" in prediction.…”

Section: A Way Forwardmentioning

confidence: 99%

Classification, Categorization, and Algorithms for Articular Cartilage Defects

2020

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There is a critical unmet need in the clinical implementation of valid preventative and therapeutic strategies for patients with articular cartilage pathology based on the significant gap in understanding of the relationships between diagnostic data, disease progression, patient-related variables, and symptoms. In this article, the current state of classification and categorization for articular cartilage pathology is discussed with particular focus on machine learning methods and the authors propose a bedside–bench–bedside approach with highly quantitative techniques as a solution to these hurdles. Leveraging computational learning with available data toward articular cartilage pathology patient phenotyping holds promise for clinical research and will likely be an important tool to identify translational solutions into evidence-based clinical applications to benefit patients. Recommendations for successful implementation of these approaches include using standardized definitions of articular cartilage, to include characterization of depth, size, location, and number; using measurements that minimize subjectivity or validated patient-reported outcome measures; considering not just the articular cartilage pathology but the whole joint, and the patient perception and perspective. Application of this approach through a multistep process by a multidisciplinary team of clinicians and scientists holds promise for validating disease mechanism-based phenotypes toward clinically relevant understanding of articular cartilage pathology for evidence-based application to orthopaedic practice.

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“…[21] Over the last decade, deep learning (DL) technologies, such as convolutional networks (CNN), restricted Boltzmann machine (RBM), recurrent neural networks (RNN), and generative adversarial network (GAN) have been gradually applied in drug design and proven to be promising approaches for artificial intelligence-based drug design. [22] , [23,24] Recently, RNN-based molecular generative network has attracted particular attentions duo to its unique features in de novo molecular design.…”

Section: Introductionmentioning

confidence: 99%

De Novo Molecular Design of Caspase-6 Inhibitors by GRU-Based Recurrent Neural Network Combined with Transfer Learning Approach

Huang

Mei

Lai-chun

et al. 2020

Preprint

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Due to the potencies in the treatments of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attentions. Herein, gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build the molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can learn accurately the SMILES grammars of about 2.4 million chemical molecules including ionic and isomeric compounds, and can generate potential caspase-6 inhibitors after transfer learning of the known 433 caspase-6 inhibitors.Further exploration of the chemical space and molecular docking showed that the generated potential inhibitors have similar chemical space distributions and binding mechanisms with the known caspase-6 inhibitors. In addition, 3 potential caspase-6 inhibitors with nanomolar-level activities were obtained and proved to be the most promising candidates for the further researches. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors.

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Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

Cited by 290 publications

References 71 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

Classification, Categorization, and Algorithms for Articular Cartilage Defects

De Novo Molecular Design of Caspase-6 Inhibitors by GRU-Based Recurrent Neural Network Combined with Transfer Learning Approach

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