Chemical Reactivity Prediction: Current Methods and Different Application Areas.

Area of Science:

• Computational Chemistry
• Drug Discovery
• Machine Learning

Background:

• Predicting chemical reactivity is vital for drug discovery, impacting synthesis, stability, pharmacokinetics, and toxicity.
• Early assessment of molecular liabilities reduces costly late-stage development failures.

Purpose of the Study:

• To review current advancements in computational reactivity prediction.
• To highlight applications in drug design, including metabolism and covalent inhibition modeling.
• To identify remaining challenges in the field.

Main Methods:

• Utilizing quantum mechanics for precise descriptions of electron and orbital interactions.
• Employing modern algorithms and increased computing power to study complex chemical systems.
• Building predictive models with machine learning using quantum mechanics and cheminformatics descriptors, even without experimental data.

Main Results:

• Computational methods now provide accurate predictions for chemical stability and reactivity.
• Machine learning models can be trained on quantum mechanical descriptors, enabling predictions without prior experimental data.
• Progress has been made in applying these methods to model drug metabolism and covalent inhibition.

Conclusions:

• Computational reactivity prediction is a rapidly advancing field with significant applications in drug design.
• Further research is needed to address unmet challenges and fully realize the potential of these methods.