AI AND MACHINE LEARNING IN ACCELERATING DRUG DESIGN: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS

The conventional drug discovery and development process has been
associated with high expenditure, long durations and low success rate, and
therefore new methods are needed. Machine Learning (ML) and Artificial
Intelligence (AI) are proving to be revolutionary measures offering a great
improvement of efficiency, accuracy and innovation as a part of the
pharmaceutical pipeline. This review is an investigation of the remarkable
alteration of AI/ML, starting with the recognition and confirmation of the
targets, and proceeding with complex molecular docking, de novo drug
design, correct ADMET (Absorption, Distribution, Metabolism, Excretion,
Toxicity) prediction. Such AI-based approaches have shown phenomenal
trends, such as 80-90% success rates in Phase I clinical trials and up to 70%
cost reduction in development timelines reduced to less than 10 years and
possibly 3-6 years. In addition, both similar and different methods in clinical
trial optimization using AI have a history of high-quality patient recruitment,
predictive modeling, adaptive designs, and the availability of digital twins
that open precision medicine. Nonetheless, there are major challenges even
despite these opportunities. These involve important data related barriers
with regard quality, quantity, diversity, privacy and security. Many AI
models are considered as the black box, which results in difficulties with
interpretability and explainability, which in turn prevents regulatory
acceptance and trust. They are also heavily burdened by large computational
demands, smooth combination with experimental methods, and regulatory,
ethical and intellectual property issues, which are developing. Future trends
are more complex algorithms such as Generative AI and Quantum
Computing, new data sharing methods such as federated learning, as well as
better integration into more advanced experimental platforms such as Organon-
a-Chip technologies. Future AI use implies that we would achieve the
full potential of AI in designing safer, more effective, and accessible
medicines in case of the stable innovation, thorough validation, transparent
governance, and effective collaboration across disciplines.