LABORATORY INFORMATICS
‘full stack’ AI-driven drug discovery firm that can go from gene to clinical candidate for any druggable target selected, Hopkins claims. ‘With the Kinetic Discovery acquisition, we now have in-house capacity to develop any assays, solve our own crystal structures and be in a strong position to build our own internal portfolio of drug candidates,’ Hopkins notes. ‘We spent the first five years focused
There’s lots of data (large and small) in the life sciences but much of it is inaccessible, unstructured and of low quality. With the shift towards ML/AI approaches the quantity and quality of data must improve to ensure that trust in algorithmic output is high
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underestimated. Exscientia has built an AI- driven drug design platform that automates the design and in silico assessment and optimisation of potentially millions of compounds against specified targets, to select the most promising candidates for further development. Steered by what the firm’s CEO Andrew Hopkins terms seasoned [human] drug hunters, the platform’s algorithms learn from the existing wealth of experimental, structural and ‘omics’ data that is already available on targets, diseases, and compound activity, and new experimentally derived data to bolster the learning dataset even further. Through this process the platform can design and then optimise candidate structures against designated targets, through design-make-test cycles. It’s a project-focused process that Hopkins maintains is faster than traditional high throughput screening-based approaches, and is significantly more likely to generate candidates that will ultimately succeed in the clinic. ‘Exscientia’s starting point was the premise that algorithmic methods can improve design efficiency through evolutionary approaches. What we asked was: how can we increase the efficiency and success rate of searching chemical space to design and optimise better drug candidates?’
Marrying human intuition with AI Traditional drug design is founded on human interpretation of available data, the formulation of a hypothesis, and the chemical structures that may have the predicted properties against the desired target, Hopkins continues. ‘This is a largely intuitive process, where you may make up to a couple of thousand molecules to solve individual problems.’ The firm’s AI-driven platforms can effectively design and pre-evaluate
12 Scientific Computing World December 2018/January 2019
“Over the next few years, as we work to improve the quality of our data even further, AI will be able to ask more sophisticated questions, such as ‘why’ someone is looking at changes to features in cells”
millions of compounds to predict efficacy, selectivity and ADME – absorption, distribution, metabolism and excretion – against any selected targets. It’s an active learning approach, rather than a deep learning approach, Hopkins says. ‘Active learning methods are about asking which experiment will provide us with the most information to answer a question.’ By asking the right questions, it can learn faster and generate a better design process.
A ‘full-stack’ drug discovery capabilities To expand its in-house laboratory capabilities, Exscientia recently acquired UK biophysics specialist Kinetic Discovery, which has added protein engineering, biophysical screening and structural biology expertise to Exscientia’s own drug design, pharmacology and computational platforms. Exscientia had been working with Kinetic Discovery through an ongoing drug discovery partnership with Evotec, and says the company is a perfect fit with its existing in-house capabilities. In combination with a recently
constructed laboratory at expanded premises on the Oxford Science Park, the acquisition of Kinetic Discovery has effectively transformed Exscientia into a
Iterative insight generation requires data, relevant context and modelling
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on technical and market validation of the approach in real-world drug discovery projects with the industry, and we are now in a position to scale the platform up.’ And with four AI-designed preclinical candidates now being developed by partners and in-house, Hopkins anticipates that the first of these will enter the clinic during 2019, adding a further layer of validation to the platform. The success of the pharma and biopharma industries – and the drugs and diagnostics that they develop – thus ultimately relies on the experimental data that they generate, the analysis and interpretation of that data, and subsequent decisions made. Tobias Kloepper, CEO at Aigenpulse, suggests it’s a workflow that should incorporate all relevant experimental and relational data generated enterprise-wide at all points in discovery and development. Aigenpulse has developed a modular, machine learning-driven platform that puts all of that experimental data and metadata in context. It applies analytical algorithms to underpin key questions with data, and enable efficient human interpretation and decision making. Historically, this level of information exploitation has not been practical, because key data often falls by the wayside. ‘Scientists haven’t had adequate computational tools,’ Kloepper comments. ‘They may store experimental
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