All Dr Raminderpal Singh (Hitchhikers AI and 20/15 Visioneers) articles
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ArticleVibe coding 101 for drug discovery scientists
Find out how AI-assisted development is democratising software creation in life sciences.
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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 ...
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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.
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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.
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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.
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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.
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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.
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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 ...
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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.
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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.
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ArticleThe evolution of AI in drug discovery: learning from history's mistakes (Part 1)
AI is transforming drug discovery, but its adoption mirrors past technological shifts in the industry. In this first part of a two-part series, we reveal Sujeegar Jeevanandam’s observations of the parallels between AI and the electronic lab notebook revolution, highlighting key challenges, lessons learned, and what the future holds for ...
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ArticleLarge language models: now more affordable and reliable than ever
In this article, Dr Raminderpal Singh explores the transformative impact of the Deepseek R1 open-source large language model on drug discovery. Its potential offers exciting opportunities for both scientists and software developers, marking a significant advancement for the life sciences community.
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ArticleScientific workflow for hypothesis testing in drug discovery: Part 2 of 3
In part two of the step-by-step scientific workflow for drug discovery series, Dr Raminderpal Singh and Nina Truter describe the functions of the workflow previously outlined and include key considerations.
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ArticleAn industry leader’s perspective on the complexity of scientific data
In this article, Dr Raminderpal Singh speaks to Janette Thomas of Five Alarm Bio for a biotech CEO’s perspective on the complexity of data faced by both large and small biotechs. Janette is on a mission to develop drugs targeting the chronic diseases associated with ageing. She shares her insights ...
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ArticleBridging science and technology: a biotech CEO’s perspective
In this article, Dr Raminderpal Singh speaks to Neil Wilkie of Mironid Ltd. for a biotech CEO’s perspective on the transformative potential of AI, and the importance of bridging communication gaps between scientific and technical teams to drive innovation and efficiency in the pharmaceutical industry.
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Article
Part four: an industry leader’s perspective on managing data quality
In this four-part series, Dr Raminderpal Singh discusses the challenges surrounding limited data quality and offers some pragmatic solutions. In this fourth article, he talks to John Conway, Chief Visioneer Officer at 20/15 Visioneers for an expert perspective.
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Article
Part three: 15 pragmatic guidelines to handle data quality issues
In this four-part series, Dr Raminderpal Singh will discuss the challenges surrounding limited data quality, and some pragmatic solutions. In this third article, he discusses pragmatic guidelines to help support better data quality.
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Article
Part two: the impact of poor data quality
In this four-part series, Dr Raminderpal Singh will discuss the challenges surrounding limited data quality, and some pragmatic solutions. In this second article, he discusses the problems that occur when using data of poor quality.
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Article
Part one: an introduction to data quality
In this four-part series, Dr Singh will discuss the challenges surrounding limited data quality, and some pragmatic solutions. In this first article, the key attributes that define data quality and its requirement for data scientists are elucidated.
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ArticlePart three: pragmatic guidelines to getting the best out of LLMs
There have been a slew of announcements over the past few months from AI-led biotechs around the potential of Large Language Models (LLM) in early drug discovery. In the third of a three-part series, Dr Raminderpal Singh presents some pragmatic guidelines for scientists in accessing and obtaining value from LLMs. ...