Abstract We explore a “best-of-both” approach to modelling molecular properties by combining learned molecular descriptors from a graph neural network…
Ground truth matters more than algorithm hype In drug discovery, we deal in imperfect data. Assays are noisy. Endpoints are…
Like all humans, drug discovery scientists suffer from inherent biases that influence our decision making. Our intuition can sometimes be…
What are parameters in machine learning models? The regular (non-hyper) parameters of an ML model are the numbers that it…
Introduction 3D molecular modelling plays a vital role in modern drug discovery, offering powerful applications to streamline research, reduce costs,…
What value does AI offer in drug discovery? The potential is huge: To learn more about the value we’re seeing…
ChatGPT can be great for the basics, but cannot replace expert human knowledge I’m going to quickly discount the most…
Introduction In early-stage drug discovery, medicinal chemists rely on predictive models to help guide which compounds to synthesise or test…
Defining value is the best place to start Before diving into the specifics of testing AI’s value, the first step…
When evaluating any new technology, it is important to establish how you will validate whether it will deliver a return…
Imagine you’re trying to find the correct key to unlock a treasure box, but there are billions of keys to…
What are neural networks? Neural networks (NNs) in various forms are very common nowadays, and specific architectures are used for…
Which AI platform do you need? The first thing you’ll need to do is decide what you need to achieve…
Benefits of continuous integration to use up-to-date data in model building Cerella has the ability to work with and learn…
Is AI-guided drug discovery faster and cheaper? The evidence for this is, by definition, anecdotal. No one runs the same…
The role of generative chemistry in drug discovery A key difficulty in finding new drugs is the sheer size of…
We’re often asked, “What’s the difference between QSAR and imputation models?”, so I’m going to explain how the methods differ, their advantages and disadvantages, and when each approach is applicable.
The Chemical Information & Computer Applications Group (CICAG) and Biological & Medicinal Chemistry Sector (BMCS) of the Royal Society of Chemistry are once again organising a conference to present the current advances in AI and machine learning in Chemistry.
Join Daniel Barr to hear more about how deep learning imputation prioritises the most relevant data, accounts for uncertainty, and guides experiment selection to bring additional value to small molecule discovery.
