All articles by Dr Raminderpal Singh – Page 2
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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.
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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 3 of 3
Drug discovery scientists develop and test complex hypotheses using data and expertise, and build workflows to support this. In this third and final article, Dr Raminderpal Singh and Nina Truter summarise the tools used in the scientific workflow – and include key considerations.
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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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ArticleScientific workflow for hypothesis testing in drug discovery: Part 1 of 3
Explore the step-by-step scientific workflow behind drug discovery, from formulating hypotheses to analysing data, ensuring accurate and reliable results.
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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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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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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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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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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. ...
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ArticlePart two: how ChatGPT enriched animal study results
Recently there has been a flurry of announcements from AI-led biotechs around the potential of Large Language Models (LLM) in early drug discovery. In the second of a three-part series, Dr Raminderpal Singh presents an example of usage of ChatGPT, which demonstrates how accessible LLMs have become for lab scientists. ...
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ArticlePart one: what can scientists do with LLMs today?
Recently there have been a flurry of announcements from AI-led biotechs around the potential of Large Language Models (LLM) in early drug discovery. In the first of a three-part series, Dr Raminderpal Singh explores what LLMs are, how early stage biotechs can take advantage of them, and what challenges they ...
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ArticleKickstarting the use of AI for biotechs: part three
Traditional wet lab scientists working on target discovery, drug identification and drug optimisation have an opportunity to catch up with their AI-enabled peers – but why should they, and how? In this article – the third of a three-part series – Dr Raminderpal Singh touches on the decisions that need ...
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ArticleKickstarting the use of AI for biotechs: part two
Traditional wet lab scientists working on target discovery, drug identification and drug optimisation have an opportunity to catch up with their AI-enabled peers – but why should they, and how? In this article – the second of a three-part series – Dr Raminderpal Singh touches on methods that are being ...
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ArticleKickstarting the use of AI for biotechs: part one
Traditional wet lab scientists working on target discovery, drug identification and drug optimisation have an opportunity to catch-up with their AI-enabled peers – but why should they, and how? In this article – the first of a three-part series – Dr Raminderpal Singh seeks to demystify the topic by outlining ...
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