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Developing new or more effective drugs for treating medicalconditions can revolutionise care, and drug discovery is a hugepart of the business of pharmaceutical companies. However, findingwhich drugs are effective for treating which conditions isdifficult. Identifying and screening candidate drugs is typicallyextremely time-consuming, which makes the search for new drugsslow, uncertain, and very expensive.

In modern science, this is not for lack of data. Plenty of dataexists on how small molecules interact with biological systems suchas proteins. However, sorting through all this data to findpromising combinations of molecules and biological pathways totreat particular conditions is very slow. Machine learning offers away to overcome this problem.

We reported recently on Alphafold a machine-learning toolcapable of predicting protein structures with much greaterreliability than previous tools. Other programs already exist thatcan predict the structures of small molecules, which are mucheasier to determine from their chemical composition than thestructures of proteins. Based on the predicted structures ofproteins and small molecules, machine-learning can predict theirinteractions, and work through libraries of molecules to identifycandidate drugs much more quickly than would be possible with humaneffort alone.

This type of processing can identify entirely novel drugs, butmay also be used to identify new applications of existing drugs.Identifying new uses of existing drugs can be particularlyvaluable, since manufacturing capacity and detailed data on sideeffects may already exist that can allow the drug to more rapidlybe repurposed to treat a new condition.

Machine learning can not only identify molecules likely tointeract with a target protein, but may also be able to extrapolateproperties such as toxicity and bio-absorption using data fromother similar molecules. In this way, machine-learning algorithmscould also effectively carry out some of the early stages of drugscreening in silico, thereby reducing the need for expensive andtime-consuming laboratory testing.

Other applications of machine learning in drug discovery includepersonalised medicine. A major problem with some drugs is thevarying response of different individuals to the drug, both interms of efficacy and side-effects. Some patients with chronicconditions such as high blood pressure may spend months or yearscycling through alternative drugs to find one which is effectiveand has acceptable side effects. This can represent an enormouswaste of physician time and create significant inconvenience forthe patient. Using data on the responses of thousands of otherpatients to different drugs, machine learning can be used topredict the efficacy of those drugs for specific individuals basedon genetic profiling or other biological markers.

Identifying candidate drugs as discussed above relies on knowingwhich biological target it is desirable to affect, so thatmolecules can be tested for their interaction with relevantproteins. However, at an even higher level, machine learningtechniques may allow the identification of entirely novelmechanisms for treating medical conditions.

Many studies exist in which participants have their genetic datasequenced, and correlated with data on a wide variety of differentphenotypes. These studies are often used to try to identify geneticfactors that affect an individual's chance of developingdisease. However, machine learning techniques can also identifycorrelations between medical conditions and other measurableparameters, such as expression of certain proteins or levels ofparticular hormones. If plausible biological pathways can bedetermined using these correlations, this could even lead to theidentification of entirely new mechanisms by which certainconditions could be treated.

Examples of AI-based drug discovery already exist in the realworld, with molecules identified using AI methods having enteredclinical trials. Numerous companies are using AI technology toidentify potential new drugs and predict their efficacy forindividual patients. Some estimates suggest that over 2 billion USDin investment funding was raised by companies in this technologyarea in the first half of 2021 alone. As with any technology,patents held by these companies allow them to protect theirintellectual property and provide security for them and theircommercial partners.

Machine learning excels at identifying patterns and correlationsin huge data sets. Exploiting this ability for drug discovery hasthe potential to dramatically improve healthcare outcomes forpatients, and streamline the unwieldy and expensive process ofdeveloping new treatments. We may stand on the threshold of a newera of personalised medicine and rapid drug development.

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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