Rapid AI generation of optimised compound designs, guided by user interaction
Pairing AI with human expertise We present a novel AI compound optimisation system, designed to include human oversight as a…
AI is not magic: A scientist’s guide to cutting through the hype
Ground truth matters more than algorithm hype In drug discovery, we deal in imperfect data. Assays are noisy. Endpoints are…
Combining deep learning with multi-parameter optimisation to predict compounds with selective activity against a broadleaf weed species
Introduction The emergence of resistance and increased stringency of regulatory requirements have created a need for new agrochemicals. The long…
Does AI remove bias in drug discovery?
Like all humans, drug discovery scientists suffer from inherent biases that influence our decision making. Our intuition can sometimes be…
Machine Learning 101: How to optimise hyperparameters
What are parameters in machine learning models? The regular (non-hyper) parameters of an ML model are the numbers that it…
How much does 3D molecular modelling software cost?
Introduction 3D molecular modelling plays a vital role in modern drug discovery, offering powerful applications to streamline research, reduce costs,…
What are the best AI drug discovery software platforms?
What value does AI offer in drug discovery? The potential is huge: To learn more about the value we’re seeing…
Will AI replace chemists?
ChatGPT can be great for the basics, but cannot replace expert human knowledge I’m going to quickly discount the most…
How can categorical models provide value in supporting compound prioritisation?
Introduction In early-stage drug discovery, medicinal chemists rely on predictive models to help guide which compounds to synthesise or test…
Test and Prove the Value of AI in Drug Discovery
Defining value is the best place to start Before diving into the specifics of testing AI’s value, the first step…
Maximising the ROI of AI – A comprehensive evaluation of Cerella for drug discovery success
When evaluating any new technology, it is important to establish how you will validate whether it will deliver a return…
How is AI transforming drug discovery chemistry?
Imagine you’re trying to find the correct key to unlock a treasure box, but there are billions of keys to…
Machine learning 101: How to build your first neural network
What are neural networks? Neural networks (NNs) in various forms are very common nowadays, and specific architectures are used for…
How much do AI drug discovery platforms cost?
Which AI platform do you need? The first thing you’ll need to do is decide what you need to achieve…
Data integration in AI-guided drug discovery
Benefits of continuous integration to use up-to-date data in model building Cerella has the ability to work with and learn…
Is AI improving drug discovery?
Is AI-guided drug discovery faster and cheaper? The evidence for this is, by definition, anecdotal. No one runs the same…
How does generative chemistry work, and how can it help me?
The role of generative chemistry in drug discovery A key difficulty in finding new drugs is the sheer size of…
What’s the difference between QSAR and imputation predictive models – which method should I use and when?
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.
