Dr Raminderpal Singh
Dr Raminderpal Singh is a recognised visionary in the implementation of AI across technology and science-focused industries. He has over 30 years of global experience leading and advising teams, helping early- to mid-stage companies achieve breakthroughs through the effective use of computational modelling. Raminderpal is currently the Global Head of AI and GenAI Practice at 20/15 Visioneers. He also founded and leads the HitchhikersAI.org open-source community and is Co-founder of the techbio, Incubate Bio.
Raminderpal has extensive experience building businesses in both Europe and the US. As a business executive at IBM Research in New York, Dr Singh led the go-to-market for IBM Watson Genomics Analytics. He was also Vice President and Head of the Microbiome Division at Eagle Genomics Ltd, in Cambridge. Raminderpal earned his PhD in semiconductor modelling in 1997 and has published several papers and two books and has twelve issued patents. In 2003, he was selected by EE Times as one of the top 13 most influential people in the semiconductor industry.
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InterviewFrom scientist to bioinformatician: how AI coding tools dissolved the activation energy barrier
A biotech CEO with decades of scientific experience but sporadic coding practice gained practical bioinformatics capabilities in six weeks using AI coding assistants.
ArticleVibe coding 101 for drug discovery scientists
Find out how AI-assisted development is democratising software creation in life sciences.
OpinionAI in drug discovery: predictions for 2026
As AI drug discovery enters 2026, the industry faces a pivotal year of clinical tests, regulatory clarity, and market consolidation. Here, Dr Raminderpal Singh examines where AI is delivering measurable gains in early discovery, where hype outpaces reality and why Phase III results will determine whether the technology can truly ...
OpinionMaking science run at the speed of thought: the reality of AI in drug discovery – Part 2
Can automation and AI finally make science run at the speed of thought? Eric Ma shares how disciplined systems, not new models, will drive the next wave of discovery.
ArticleMaking science run at the speed of thought: the reality of AI in drug discovery – Part 1
Everyone talks about AI speeding up drug discovery, but Eric Ma explains why, without clean data and statistical discipline, it can actually do the opposite.
ArticleThe predictive validity crisis: Pharma’s productivity paradox – Part II
Part II shows that the predictive validity crisis can be solved by rethinking how the industry chooses models, measures outcomes and integrates systems. Success stories from Vertex, Regeneron and AstraZeneca illustrate how aligning biology, measurement and strategy can reverse decades of declining productivity.
ArticleThe predictive validity crisis: Pharma’s productivity paradox – Part I
Drug discovery now costs 100 times more per FDA-approved drug than in 1950, despite vast advances in biology and computing. The core problem is the collapse of predictive validity in preclinical models, which sits at the heart of pharma’s productivity paradox.
ArticleBeyond the hype: a veteran’s honest assessment of AI in drug discovery – Part 3
AI is starting to transform drug discovery, but progress is still slow and big challenges remain. Thibault Géoui explores the gaps, hurdles and breakthroughs needed before it can truly change pharma R&D.
ArticleBeyond the hype: a veteran's honest assessment of AI in drug discovery - Part 2
Thibault Géoui explains why AI could finally help pharma overcome its productivity crisis and why the payoff won’t come as quickly as the optimists claim.
ArticleBeyond the hype: a veteran's honest assessment of AI in drug discovery - Part 1
An interview with Thibault Géoui reveals why this technology wave might finally break through pharma’s productivity crisis – and why it will take longer than the optimists claim.
ArticleFixing failed drugs: AI solutions for toxicity in drug discovery – part 3
What role could large language models and AI agents play in drug safety? In Part 3, Layla Hosseini-Gerami of Ignota Labs discusses how emerging technologies might make toxicity analysis faster, more accessible and part of the drug discovery workflow from day one.
ArticleFixing failed drugs: AI solutions for toxicity in drug discovery – part 2
Why do so many drug candidates fail before reaching patients – and can AI help stop the losses? In Part 2, Layla Hosseini-Gerami of Ignota Labs outlines the scope of the toxicity problem and explains why failures often come too late to fix.
ArticleFixing failed drugs: AI solutions for toxicity in drug discovery – part 1
Why do so many drug candidates fail before reaching patients – and can AI help stop the losses? In Part 1, Layla Hosseini-Gerami of Ignota Labs outlines the scope of the toxicity problem and explains why failures often come too late to fix.
ArticleSolving the disconnect between lab and data scientists: part 2
As the lab–data science divide continues, Ian Kerman looks ahead to a future of deeper collaboration – one where shared skills, smarter tools and a shift in mindset could finally break down the barriers. In this second interview, he shares his vision, practical ideas and advice for the next generation ...
ArticleSolving the disconnect between lab and data scientists: part 1
Lab scientists and data scientists often speak different languages and that miscommunication can slow down important research. In this interview, Ian Kerman shares how his team is working to break down those walls and spark better collaboration.
ArticleHow AI and LLMs are transforming drug discovery: part 2
As AI reshapes scientific work, two founders debate how best to build tools scientists can trust — should we embed expertise into the model or the team? From agent-powered labs to hypothesis-generating machines, the future of drug discovery is being reimagined right now.
ArticleHow AI and LLMs are transforming drug discovery: part 1
AI is reshaping drug discovery – but not without resistance. In this two-part conversation, André França and Eli Pollock share honest insights about the real barriers to AI adoption in life sciences and how embedding domain expertise into AI workflows might be the key to unlocking its full potential.
ArticleUsing knowledge graphs in drug discovery (Part 2): how they’re shaping scientific progress
In this second interview of the series, Andreas Kolleger, Head of GenAI Innovation at Neo4j, discusses how knowledge graphs and AI are transforming scientific discovery and improving life sciences workflows.
ArticleUsing knowledge graphs in drug discovery (Part 1): how they link to large language models
In this first interview of a two-part series, Andreas Kolleger explores the convergence of knowledge graphs and large language models. As the head of GenAI innovation at Neo4j, Andreas brings a unique cross-industry perspective on how these technologies can enhance life sciences workflows.
ArticleThe evolution of AI in drug discovery: learning from history's mistakes (Part 2)
In this second part of a two-part series, we continue Sujeegar Jeevanandam’s exploration of the future of AI in drug discovery. We share his vision for transformative AI applications, such as simulating human pharmacokinetics and pharmacodynamics, and offer strategic recommendations for biotechs looking to adopt AI.