Authors: Ayush Patel, Sangeeta Vhatkar, Namdeo Badhe
Abstract: AI and machine learning are shaping up drug discovery and it is about time. The old way- slow, expensive and full of dead-ends- are outdated. Tools like deep learning, graph neural networks, GANs and reinforcement learning are stepping up. These tools actually help scientists spot new targets, sift through virtual libraries for promising compounds, predict how molecules will behave, dream up brand new drug designs, find fresh uses for old drugs and even streamline clinical trials. Graph models, in particular, shine because they get the complicated shape and connections in molecules. These all let researchers simulate how tiny structures interact in the messy reality of biology. Generative AI pushes boundaries even further by designing all sorts of molecules- each tailored for certain properties- across an almost endless chemical universe. Technology is making and creating waves everywhere: cancer, heart conditions, brain disorders, infections-you name it. Across the board, the results are better predictions, smarter trade-offs, more molecular variety and a smoother path from lab to clinic. Of course, it’s not all smooth sailing. Challenges remain like messy data, black-box designing making, regulatory headaches and the tricky business of converting code into medicine. But even with those bumps, AI-powered drug discovery isn’t another upgrade. It is a real-shift: more data-driven, more scalable and a lot more personal. The evidence keeps piling up-AI is speeding up therapeutic breakthroughs and rewriting the future position of medicine, one algorithm at a time.
DOI: https://doi.org/10.5281/zenodo.19663484
