Artificial Intelligence-Based Drug Design and Discovery

The drug discovery process from hit-to-lead has been a challenging task that requires simultaneously optimizing numerous factors from maximizing compound activity, efficacy to minimizing toxicity and adverse reactions. Recently, the advance of artificial intelligence technique enables drugs to be efficiently purposed in silico prior to chemical synthesis and experimental evaluation. In this chapter, we present fundamental concepts of artificial intelligence and their application in drug design and discovery. The emphasis will be on machine learning and deep learning, which demonstrated extensive utility in many branches of computer-aided drug discovery including de novo drug design, QSAR (Quantitative Structure–Activity Relationship) analysis, drug repurposing and chemical space visualization. We will demonstrate how artificial intelligence techniques can be leveraged for developing chemoinformatics pipelines and presented with real-world case studies and practical applications in drug design and discovery. Finally, we will discuss limitations and future direction to guide this rapidly evolving field

ugtm: A Python Package for Data Modeling and Visualization Using Generative Topographic Mapping

ugtm is a Python package that implements generative topographic mapping (GTM), a dimensionality reduction algorithm by Bishop, Svensén and Williams. Because of its probabilistic framework, GTM can also be used to build classification and regression models, and is an attractive alternative to t-distributed neighbour embedding (t-SNE) or other non-linear dimensionality reduction methods. The package is compatible with scikit-learn, and includes a GTM transformer (eGTM), a GTM classifier (eGTC) and a GTM regressor (eGTR). The input and output of these functions are numpy arrays. The package implements supplementary functions for GTM visualization and kernel GTM (kGTM). The code is under MIT license and available on GitHub (https://github.com/hagax8/ugtm). For installation instructions and documentation, cf. https://ugtm.readthedocs.io.   Funding statement: HG acknowledges funding from the US National Institute of Mental Health (PGC3: U01 MH109528)

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics

DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

Abstract We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability