Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Yang, Xin; Wang, Yifei; Byrne, Ryan; Schneider, Gisbert; Yang, Shengyong

doi:10.1021/acs.chemrev.8b00728

Cited by 680 publications

(467 citation statements)

References 811 publications

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“…In total, 4,922 unique valid structures were automatically generated and all matched the defined rules by using ADQN-FBDD without any pre-training as many other methods need. 35,40,41,54,55 Next, All the molecules with high deep reinforcement learning scores (DRL score: R(S)>0.6) were kept (47 molecules). Then, these 47 unique molecules were prepared to generate at least 1 conformation with the local energy minimization using the OPLS-2005 force field by the "ligand prepare" module of Schrödinger 2015 software.…”

Section: Molecular Generation and Selectionmentioning

confidence: 99%

AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2

Tang

Liu

et al. 2020

Preprint

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The focused drug repurposing of known approved drugs (such as lopinavir/ritonavir) has been reported failed for curing SARS-CoV-2 infected patients. It is urgent to generate new chemical entities against this virus. As a key enzyme in the life-cycle of coronavirus, the 3C-like main protease (3CL pro or M pro ) is the most attractive for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CL pro . We obtained a series of derivatives from those lead compounds by our structure-based optimization policy (SBOP). All the 47 lead compounds directly from our AI-model and related derivatives based on SBOP are accessible in our molecular library at https://github.com/tbwxmu/2019-nCov. These compounds can be used as potential candidates for researchers in their development of drugs against SARS-CoV-2.author/funder. All rights reserved. No reuse allowed without permission. : bioRxiv preprint disease (COVID-19) worldwide. 1 As of March 2, 2020, more than 90,000 people have been infected by SARS-CoV-2 and more than 3000 people have been reported dead according to Johns Hopkins Coronavirus map tracker. 2 The numbers of infection and death are still increasing. To face the considerable threat of SARS-CoV-2, it is urgent to develop new inhibitors or drugs against this deadly virus. Unfortunately, since the outbreak of severe acute respiratory syndrome (SARS) eighteen years ago, there has been no approved treatment against the SARS coronavirus (SARS-CoV), 3 which is similar to SARS-CoV-2. Repurposing potential drugs such as lopinavir and ritonavir also failed to SARS-CoV-2 injected patients. 4 Structure-based antiviral drug design with a new artificial intelligence algorithm may represent a more helpful approach to get the SARS-CoV-2 targeted inhibitors or drugs. Thanks to the prompt efforts of many researchers, we have several pieces of important information about this vital virus genome and protein structures. We now know that the non-structural protein 5 (Nsp5) is the main protease (M pro ) of SARS-CoV-2 and it is a cysteine protease, which also been called "3C-like protease" (3CL pro ). Moreover, we know that the 3D structure of 3CL pro is very similar to SARS-CoV with a sequence identity of >96% and 3D structure superposition RMSDCα of 0.44 Å as shown in Figures S1 and S2. 3CL pro has been reported as an attractive target for developing anti-coronaviral drugs: 1) this protease is highly conserved in both sequences and 3D structures; 5 2) 3CL pro is a key enzyme for related virus (including SARS and SARS-CoV-2) replication; 3) it only exists in the virus, not in humans. Developing specific antiviral drugs targeting 3CL pro of the specific virus has shown significant success; for example, both approved drugs lopinavir and ritonavir can completely occupy the substrate-binding site of 3CL pro to break down the replication of human immunodeficien...

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Section: Molecular Generation and Selectionmentioning

confidence: 99%

AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2

Tang

Liu

et al. 2020

Preprint

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“…Attempting the latest DNN techniques such as RNN with LSTM and CNN [5][6][7][8] may be possible to dramatically improve predictive performance. In particular, we focus on the graph convolutional neural networks.…”

Section: Limit Of Descriptorsmentioning

confidence: 99%

“…[1] In fact, deep neural networks (DNN) approaches in the Kaggle "Merck Molecular Activity Challenge (MMAC)" competition held in 2012 [2] and the post analysis [3,4] showed better predictive performance than the conventional methods like random forest (RF) and support vector machine (SVM). The success of the QSAR/DNN approaches was a significant impact on many researchers in chemistry and pharmaceutical fields, and created a major trend that followed to apply many recent artificial neural network (ANN) techniques to drug and material discovery, such as Recurrent Neural Network (RNN) with long short-term memory cells (LSTM) and Convolutional Neural Network (CNN) [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Validation Study of QSAR/DNN Models Using the Competition Datasets

Kato

Hamada

Goto

2019

Molecular Informatics

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Since the QSAR/DNN model showed predominant predictive performance over other conventional methods in the Kaggle QSAR competition, many artificial neural network (ANN) methods have been applied to drug and material discovery. Appearance of artificial intelligence (AI), which is combined various general purpose ANN platforms with large-scale open access chemical databases, has attracting great interest and expectation in a wide range of molecular sciences. In this study, we investigate various DNN settings in order to reach a high-level of predictive performance comparable to the champion team of the competition, even with a general purpose ANN platform, and introduce the Meister setting for constructing a good QSAR/DNNs model. Here, we have used the most commonly available DNN model and constructed many QSAR/DNN models trained with various DNN settings by using the 15 datasets employed in the competition. As a result, it was confirmed that we can constructed the QSAR/DNN model that shows the same level of R2 performance as the champion team. The difference from the DNN setting recommended by the champion team was to reduce the mini-batch size. We have also explained that the R2 performance of each target depends on the molecular activity type, which is related to the complexity of biological mechanisms and chemical processes observed in molecular activity measurements.

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“…As larger amounts of training data (molecules and their protein binding partners) have become publicly available, ligand-based predictions of polypharmacology have expanded from classification of binding (e.g. active/inactive) to regression of drug-target affinity scores (e.g., Ki, IC50) 3,4,[8][9][10][11][12] . These models exploit the similar property principle of chemical informatics, which states that small molecules with similar structures are likely to exhibit similar biological properties, such as their binding to protein targets 13 .…”

Section: Introductionmentioning

confidence: 99%

“…A substantial literature focuses on correcting the balance of positive to negative examples (here, binders to non-binders) in machine learning training datasets as well as addressing dataset sparsity 12,[18][19][20][21][22][23][24][25] . These corrections primarily adopt majority-or minority-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Adding stochastic negative examples into machine learning improves molecular bioactivity prediction

Cáceres

Mew

Keiser

2020

Preprint

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Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological datasets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios whose characteristics differ from a random split of conventional training datasets. We developed a pharmacological dataset augmentation procedure, Stochastic Negative Addition (SNA), that randomly assigns untested molecule-target pairs as transient negative examples during training. Under the SNA procedure, ligand drug-screening benchmark performance increases from R 2 = 0.1926 ± 0.0186 to 0.4269±0.0272 (121.7%). This gain was accompanied by a modest decrease in the temporal benchmark (13.42%). SNA increases in drug-screening performance were consistent for classification and regression tasks and outperformed scrambled controls. Our results highlight where data and feature uncertainty may be problematic, but also show how leveraging uncertainty into training improves predictions of drug-target relationships.

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Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Cited by 680 publications

References 811 publications

AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2

AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2

Validation Study of QSAR/DNN Models Using the Competition Datasets

Adding stochastic negative examples into machine learning improves molecular bioactivity prediction

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