I am a Master's student at King's College London researching the application of artificial intelligence in pharmaceutical drug discovery. For my dissertation project, I am conducting interviews with companies at the forefront of this field to understand better the current drug discovery landscape in the UK and how AI is transforming research processes.
I've interviewed several companies which are leveraging AI and machine learning technologies to accelerate drug discovery. My research interests are at the intersection of technology, the life sciences, and public policy. For this project, I aim to identify best practices in AI for drug discovery and better understand the challenges and opportunities of implementing these advanced techniques.
This research stems from the concerns raised when a generative algorithm developed VX and tens of thousands of analogues. https://pubmed.ncbi.nlm.nih.gov/36270472/
I was wondering if anybody in this area would be up for a discussion or knows of anybody in this field. The interview would be no longer than 30 mins!
Thanks :)
Cool topic! I was reading and thinking about this topic recently and so can list some links you might find interesting. Don't know any experts personally. I have a few thoughts on comparing ML advances in ligand-protein modelling to protein-protein interactions, as the former is most relevant for drug discovery and further along in development, while the later is probably coming soon and has maybe larger dual-use risk. But those thoughts are under-developed and liable to change, so I won't inflict them on you. Here are some links I liked!
[edit: just today Jonas Sandbrink put up a preprint that I expect will be very useful: https://arxiv.org/abs/2306.13952]
Hershberg 2023 - Machine brains and their discontents --> recent, reader-friendly, high-level
Also Herschberg 2021 - Optimising viral vehicles --> on applying ML to optimising adenovirus capsids - i.e. viral engineering
Also good: Nature Editorial, 2023 - For chemists, the AI revolution has yet to happen
Derek Lowe also has a bunch of interesting and sensible blogposts:
https://www.science.org/content/blog-post/deliberately-optimizing-harm
https://www.science.org/content/blog-post/computing-our-way-antibodies
https://www.science.org/content/blog-post/computing-your-way-protein-binders
https://www.science.org/content/blog-post/virtual-screening-versus-numbers
And finally some papers on the challenges, potential, current state of the field, and cutting edge applications:
RFdiffusion - https://www.biorxiv.org/content/10.1101/2022.12.09.519842v1
Johnston et al. 2022 - Machine Learning for Protein Engineering
Alley 2020 - Low-N protein engineering with data-efficient deep learning
Yang et al. 2020 - Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets
Weinstein et al. 2023 - Designed active-site library reveals thousands of functional GFP variants
Hie et al. 2023 - Efficient evolution of human antibodies from general protein language models
Volkov et al. 2022 - On the Frustration to Predict Binding Affinities from Protein–Ligand Structures with Deep Neural Networks
Madani et al. 2023 - Large language models generate functional protein sequences across diverse families