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Generative AI Models for Drug Discovery

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Biophysical and Computational Tools in Drug Discovery

Part of the book series: Topics in Medicinal Chemistry ((TMC,volume 37))

Abstract

A drug-like-molecule library can contain 1023–1060 molecules, among which only approximately 1012 molecules may be synthesized in labs. However, it is still challenging for researchers to find the most promising candidates among the vast number of synthesizable compounds in a reasonable time. Moreover, although molecules are picked for their predicted bioactivities, their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are often difficult to predict and modify. This is often a bottleneck for downstream studies and applications. It would be more productive if candidate molecules are generated, rather than screened from libraries, with suitable ADMET properties as prerequisites at the beginning of the molecule design process. Recently, artificial intelligence (AI)-based generative models have been described for designing drug candidates using prior biological and chemical knowledge. A spectacular example was the use of a combination of AI generative techniques and reinforcement learning by the biotechnology company, Insilico Medicine, to successfully create new DDR1 kinase inhibitors to treat fibrosis in only 21 days. We will describe how reinforcement learning (RL) algorithms can be applied to generative AI for better real-world effectiveness while better utilizing modern distributed hardware assets. In this chapter, we will review simple and advanced AI generative models and discuss the advantages and disadvantages of each model.

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Abbreviations

3D-CNN:

3D convolutional neural networks

AAE:

Adversarial auto-encoders

ADME:

Absorption, distribution, metabolism, and excretion

ADQN-FBDD:

Advanced deep Q-learning neural network with fragment-based drug discovery

AI:

Artificial intelligence

AIDD:

Artificial intelligence drug discovery

APEX-FBDD:

The distributed version of ADQ-FBDD

CASP:

Critical assessment of protein structure prediction

COVID19:

Coronavirus disease 2019

CVAE:

Conditional variational auto-encoder

DAI:

Distributed artificial intelligence

DQN:

Deep Q-learning neural network

FCN:

Fully connected network

GAN:

Generative adversarial networks

GCN:

Graph convolutional network

GENTRL:

Generative tensorial reinforcement learning

GRU:

Gated recurrent units

KL Divergence:

Kullback-Leibler divergence

LSTM:

Long short-term memories

MDP:

Markov decision process

QED:

Quantitative estimation of drug-likeness

R&D:

Research and development

RL:

Reinforcement learning

RNN:

Recurrent neural network

SAMPN:

Self-attention message passing graph neural network

SOM:

Self-organizing map

USD:

United States dollar

VAE:

Variational auto-encoder

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  1. Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, KS, USA

    Bowen Tang, John Ewalt & Ho-Leung Ng

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Correspondence to Ho-Leung Ng .

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  1. GIPER, Kashipur, India

    Anil Kumar Saxena

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Tang, B., Ewalt, J., Ng, HL. (2021). Generative AI Models for Drug Discovery. In: Saxena, A.K. (eds) Biophysical and Computational Tools in Drug Discovery. Topics in Medicinal Chemistry, vol 37. Springer, Cham. https://doi.org/10.1007/7355_2021_124

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