About this Research Topic

Background

Computational approaches to studying drug toxicity have been and continue to be an important tool in drug development, and these methods have substantially advanced in the last 5 to 10 years. Toxicity concerns identified during optimisation and development including bioactivity, drug-drug interactions, and drug-induced liver injury can prevent strong therapeutic candidates proceeding. As an example, covalent inhibitors present several appealing properties relative to non-covalent inhibitors, but require greater consideration of potential off-target toxicity. Inability to satisfy drug safety criteria is a primary driver of drug withdrawal and termination during clinical development. Other common toxicity vectors include reactivity and reactive metabolite formation, enzyme inactivation, and bioactivation mechanisms.

Our goal is to review some of the key advances in computational study of drug toxicity, especially those that may be pragmatically useful in drug development. For example, Artificial intelligence is increasingly used to satisfy this need and advances in machine learning are leveraged to speed up drug candidate identification and optimization. In contrast, structural filters are commonly used to flag problematic candidates. However, these filters are determined retrospectively and lack predictive power for new and understudied motifs and consideration of downstream metabolic processes. AI-based tools are useful not just for their predictive capabilities, as in molecular property prediction, but also for their generative capabilities, as in the facilitation of de novo design. There are several other methods and subtasks relevant to this overall goal, and this example is presented merely for illustration.

This Research Topic aims to cover advancements in computational applications in the context of drug toxicity and its relevance during lead-finding, lead-optimization, screening, and modeling of metabolism and pharmacology, among other themes. The special focus is on small molecules and machine learning approaches, but we also invite researchers to consider other biopharmaceuticals and algorithmic approaches within cheminformatics. In addition to in silico developments, we welcome supporting submissions with in vitro and/or in vivo experimental validation and results. Example themes covered in this Research Topic include, but are not limited to:

- Adverse drug reactions
- Cataloguing/mining drug databases that facilitate toxicity research and library design
- Clinical trial analysis
- Computational toxicity screening and toxicity/bioactivity prediction
- Enzyme inactivation
- De novo design and repurposing
- Drug-drug interactions (DDI)
- Drug-induced liver injury (DILI)
- Metabolic transformations
- Molecular property prediction
- Molecular optimization strategies
- Molecular representation learning
- Reactive metabolite formation
- New models and methodologies for studying drug metabolism

Keywords: Machine Learning, Deep Learning, Generative Models, Graph neural networks, Toxicity, Reactive Metabolite Formation, Bioactivation, Open Drug Database Mining, Molecular Property Prediction, Molecular representation learning, Computational screening

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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