<p></p><p>Designing proteins that have never existed in nature is no longer sci-fi — it’s becoming a real drug discovery strategy. In this episode, Kashif Sadiq, Founder &amp; CEO of DenovAI Biotech, explains how AI is powering a shift from searching for biologic binders to intentionally designing new proteins from scratch.</p><p>Kashif shares his journey from studying physics at University of Cambridge into computational biophysics, and how breakthroughs like AlphaFold from DeepMind helped unlock the next frontier: <em>de novo</em> protein design. Instead of hoping evolution has already produced a usable molecule, Kashif describes how modern AI can engineer bespoke proteins for specific functions, including challenging targets where traditional approaches come up short.</p><p>The conversation dives into the sheer scale of “protein space” and why evolution has only explored a tiny fraction of what’s possible. Kashif outlines how this opens the door to targeting diseases and biological mechanisms that have historically been considered undruggable, especially where flat protein interfaces or complex signalling pathways have made small molecules ineffective.</p><p>Finally, Kashif explains why combining generative AI with physics-based methods is essential to reduce false positives, improve real-world binding performance, and enable “one-shot design” — where discovery and optimisation become a single integrated process. He also shares what keeps him up at night: clinical trial attrition — and why designing better earlier may be the key to improving success later.</p><p><strong>Topics Covered</strong></p><ul><li><p>De novo protein design vs traditional biologics discovery</p></li><li><p>Why evolution explored only a tiny fraction of protein space</p></li><li><p>“Programmable biologics” and intentional molecular design</p></li><li><p>Alpha Design and designing proteins from the inverse problem</p></li><li><p>Antibodies, nanobodies, and therapeutic protein engineering</p></li><li><p>Combining generative AI with physics-based validation</p></li><li><p>Reducing false positives in protein binding predictions</p></li><li><p>“One-shot design” and compressing discovery timelines</p></li><li><p>Undruggable targets, flat interfaces, and intracellular signalling</p></li><li><p>Clinical trial attrition and what’s missing at the preclinical stage</p></li><li><p>When the first de novo-designed therapeutic could enter trials<br /></p></li></ul><p><strong>About the Podcast</strong></p><p>AI for Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.</p><p>This show blends deep sector experience with practical, no-fluff conversations that demystify AI for biopharma execs — from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&amp;D, clinical trials, market access, medical affairs, regulatory, insights, sales, marketing, and more.</p><p><br /></p><p><strong>Dr. Andree Bates</strong><a href="https://www.linkedin.com/in/dr-andree-bates/" rel="ugc noopener noreferrer" target="_blank"><strong> LinkedIn</strong></a><strong> |</strong><a href="https://www.facebook.com/TheAmeliaAI/" rel="ugc noopener noreferrer" target="_blank"><strong> Facebook</strong></a><strong> |</strong><a href="https://x.com/TheAmeliaAI" rel="ugc noopener noreferrer" target="_blank"><strong> </strong><strong>X</strong></a><strong></strong></p><p><br /></p>