PredPS: Attention-based graph neural network for predicting stability of compounds in human plasma

Advancing drug discovery with deep attention neural networks

In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our approach to complex data. This review explores the attention mechanism and its extended architectures, including graph attention networks (GATs), transformers, bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPTs) and bidirectional and auto-regressive transformers (BART). Delving into their core principles and multifaceted applications, we uncover their pivotal roles in catalyzing de novo drug design, predicting intricate molecular properties and deciphering elusive drug-target interactions. Despite challenges, these attention-based architectures hold unparalleled promise to drive transformative breakthroughs and accelerate progress in pharmaceutical research.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

A molecular docking exploration of the large extracellular loop of tetraspanin CD81 with small molecules

Tetraspanin CD81 is a transmembrane protein used as a co-receptor by different viruses and implicated in some cancer and inflammatory diseases. The design of therapeutic small molecules targeting CD81 lags behind monoclonal antibodies and peptides but different synthetic and natural products binding to CD81 have been identified. We have investigated the interaction between synthetic compounds and CD81, considering both the cholesterol-bound full-length receptor and a truncated protein corresponding to the large extracellular loop (LEL) of the tetraspanin. They represent the closed and open conformations of the protein, respectively. Stable complexes were characterized with bi-aryl compounds (notably the quinolinone-benzothiazole 6) and atypical molecules bearing a 1-amino-boraadamantane scaffold well adapted to interact with CD81 (5a-d). In each case, the mode of binding to CD81 was analyzed, the binding sites identified and the molecular contacts determined. The narrow intra-LEL binding site of CD81 can accommodate the elongated bi-aryl 6 but not a series of isosteric compounds with a bis(bicyclic) scaffold. The bora-adamantane derivatives appeared to bind well to CD81, but essentially to the external surface of the protein loop. The binding selectivity of the compounds was assessed comparing binding to the LEL of tetraspanins CD81, CD9 and Tspan15. A net preference for CD81 over CD9 was evidenced, but the LEL of Tspan15 also provided a suitable binding site for the compounds, notably for the bora-adamantane derivatives. This work provides an aid to the identification and design of tetraspanin-binding small molecules, underlining the distinct behavior of the open and closed conformation of the protein for drug binding.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00203-6.

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

Attention is all you need: utilizing attention in AI-enabled drug discovery

Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.