A practical guide to implementing AI
In this ebook, you’ll discover the key considerations which every leader needs to take in order to successfully implement AI in their drug discovery pipelines.
Imagine you’re trying to find the correct key to unlock a treasure box, but there are billions of keys to choose from. You don’t know which one will work, and all you can do is test them endlessly one by one. This is what drug discovery has traditionally been like for scientists – a painstaking and expensive process that could take years often ends in disappointment.
The emergence of Artificial Intelligence (AI) arrives with revolutionary power in drug discovery – it doesn’t just promise to speed up the process but also transform it. People are realising the potential of AI to reduce time, money, and resources. In chemistry alone, from generating new molecules tailored to specific needs (generative chemistry) to mapping out the most efficient ways to create them (synthesis planning) and predicting their performance realistically (compound property prediction), AI is accelerating every step of the drug discovery pipeline.
In this blog, we’ll dive into these three impactful areas and discover the role of AI in drug discovery chemistry.
Generative chemistry – AI-powered molecule design
Exploring and targeting the correct active compounds with suitable pharmacokinetic properties in vast chemical space is challenging. Early generative chemistry often provided mixed-quality suggestions. Chemists were forced to spend hours filtering through suggestions of unstable, inappropriate or synthetically complex molecules to find their useful series and chemistries.
Numerous generative chemistry methods are available, from classical models, where medicinal chemistry transformations are iteratively applied, to AI and machine learning approaches such as auto-encoders. The latest generative chemistry methods tend to use transformer models, the foundation of large language models (LLMs), such as ChatGPT. They are quicker, cheaper to train and more powerful than other methods – they can work with larger datasets. These models enable scientists to generate a broader variety of chemical structures with greater confidence in their synthetic accessibility.
The best methods in generative chemistry are those which harness the user’s expert knowledge, in a concept which we call Augmented Chemistry®. By combining AI’s ability to explore diverse strategies without bias with the intuition and experience of expert scientists, we can unlock new possibilities in drug discovery and development. To learn more about augmenting inspiration with generative chemistry, read Matt’s IBI article.
Synthesis planning – AI as the master molecular chef
Designing a molecule is one thing, but figuring out how to actually make it is another challenge. This is where AI steps in with synthesis planning – creating a recipe for how to build a molecule.
Traditionally, scientists relied heavily on their expertise and long literature searches to figure out the best way to synthesise a compound by trial and error. Now, AI emerges as the hero in this area, not only collating literature much more quickly but also predicting synthetic routes in minutes. Some AI methods even integrate direct links to vendor libraries – with all the information about stock, price, and delivery times – allowing synthesis to begin as soon as possible.
Nonetheless, the intricacies of identifying a feasible synthetic route can be tricky for current AIs to predict: Can AI propose novel synthetic routes beyond the reactions it was trained on? Are the suggested routes scalable in large-scale manufacturing? This field is evolving rapidly, with exciting research underway, and we anticipate significant advancements in the near future.
Compound property prediction – AI enables smarter and better decisions
AI can help you predict your compound’s properties – that’s super. But how does it compare to traditional models?
Whilst traditional QSAR methods rely on molecular descriptors, advanced deep-learning imputation approaches like Cerella(TM) can leverage existing experimental data to support predictions (see our recent blog on QSAR vs imputation). It can also tell you about the confidence in each prediction; which additional data you should obtain to support compound prioritisation; and which of your current experimental measured data points may require re-evaluation due to potential errors.
By providing information on uncertainty, such methods can guide you not only to the compounds with the best predicted properties but also to those which are most likely to succeed in your project. To learn more, you can read a case study by Open Source Malaria or watch our webinar.
Beyond basic predictions, AI can identify and harness complex relationships in large, sparse and noisy data sets, which are common in many drug development organisations. By doing so, AI can inform predictions that generate new insights and support your understanding of mechanisms of action or adverse outcome pathways. AI methods can identify the early-stage frequently measured endpoints that are most correlated with late-stage endpoints. By suggesting cheaper early-stage measurements that inform predictions of expensive late-stage outcomes, AI can guide your decisions on compound progression. This can enable you to minimise expensive late-stage experiments.
AI in chemistry – An ally, not a replacement
AI isn’t here to take over our jobs – it’s here to make them better. It is about embracing and accepting the role of AI in your drug discovery journey. If you feed your AI relevant data, it should recognise patterns, make suggestions and predictions, and help you guide compound, assay or experiment prioritisation more effectively. To unlock the full value AI can deliver, we should be open to adapting our workflows and rethinking how we integrate technology into our research.
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About the author
Tamsin Mansley, PhD
President, Optibrium Inc. and Global Head of Application Science
Tamsin holds a PhD in Organic Chemistry from University of East Anglia in the UK and pursued Postdoctoral studies in the labs of Prof. Philip Magnus at University of Texas, Austin.
She is an experienced drug discovery scientist, having worked as a medicinal chemist at Eli Lilly and UCB Research. Her interests lie in coupling machine learning and artificial intelligence techniques with generative chemistry approaches to explore chemistry space and guide compound design.
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