How I Built a Generative AI Model that can Generate Novel Small Molecules for Drug Discovery! — Part 1: Data Preprocessing
<p>Recently, MD Anderson, the world’s best cancer treatment facility, announced that it was partnering with Generate:Biomedicine, an Artificial Intelligence (AI) drug development company, to take advantage of the rise of generative AI to produce “novel protein therapeutics.” They aim to speed up the drug development process by optimizing the road from proof-of-concept to clinical trials using gen AI (<a href="https://www.mdanderson.org/newsroom/md-anderson-generate-biomedicines-co-develop-protein-therapies-cancer-using-generative-ai.h00-159617856.html" rel="noopener ugc nofollow" target="_blank">Source</a>).</p>
<p>That’s cool and all, but why is MD Anderson doing this, and how can AI help?</p>
<p>Drug development is a complex and lengthy process involving the discovery, testing, and approval of new medications to treat various diseases. It typically consists of stages like target identification, compound design, preclinical testing, and rigorous clinical trials before FDA approval. It’s a time-consuming and resource-intensive endeavor, with most potential drug candidates failing to progress at each step.</p>
<p><a href="https://medium.com/@himynameisaftab/how-i-built-a-generative-ai-model-that-can-generate-novel-small-molecules-for-drug-discovery-d29c58be0174"><strong>Website</strong></a></p>