Revolutionizing Drug Development: AI-Driven Predictive Modeling for Accelerated Small Molecule and Biologic Therapeutics

Abstract

AI is hailed as the most powerful modern tool for discovering and developing small molecules and biological therapeutics. Emphasis is placed on the more traditional drug development process where a molecule is discovered, developed, tested, and marketed to consumers, signaling key challenges of inefficiency and high failure rates. The application of big data and artificial intelligence in the drug discovery process helps to predict targets, do the screenings, and diagnose drugs. Supervised and unsupervised learning, reinforcement, and convolutional and recurrent neural networks are some machine learning algorithms applied to intricate biological data. Transformer models are also used in drug discovery while large-scale genomic and other proteomic studies are performed. Molecular informatics is an example of gathering multitudinous data types and genomics, proteomics, metabolomics, and clinical trials. Data preprocessing enhances the effectiveness of the algorithm and model by providing accurate results while using cross-validation to help identify a model that generalizes to unseen data. AI also helps speed up the discovery and development of new drug molecules, determine their binding affinities, and improve the PK/PD profiles. In this vein, AI discovered Baricitinib as a therapeutic for COVID-19, which is one of the ways to repurpose drugs.

Saved several ways and hours in biological therapeutics, including antibody and protein engineering, to determine binding affinity, select the best suitable candidates, and maintain stability. Amgen is one company that employs AI in designing proteins, especially entirely new proteins that will majorly treat autoimmune diseases. Although the use of Artificial Intelligence in drug development has brought about many benefits, the following ethical issues are evident: Such regulatory bodies as the FDA and EMA are coming up with guidelines through which AI use in drug development can be safe and effective. The future regulatory measures will be developed according to the validation of the models used, data protection, and cybersecurity aspects. Regulators and industries, coupled with universities, shall play a significant role in enhancing the development of the pharmaceutical application of AI.