New antibiotics capable of killing drug-resistant gonorrhoea have been developed by AI. Experts believe that Artificial Intelligence could signify a'second golden age' of antibiotic discovery, after creating two drugs that could be capable of killing superbugs such as gonorrhea and MRSA. Led by Professor James Collins at the Massachusetts Institute of Technology (MIT), a specialist research team used generative AI algorithms to interrogate 36million compounds. The experts then trained the AI to help it learn how bacteria was affected by different molecular structures built of atoms in order to design new antibiotics. In order to do this, they gave it the chemical structure of known compounds and data on their ability to hinder the growth of different bacteria species. Throughout the study, published in the journal Cell, anything too similar to the current antibiotics available, or with the potential to be toxic to human beings, was eradicated.

Artificial intelligence (AI) is a broad and evolving scientific field, and the value it can deliver at various stages of the drug discovery process is now widely accepted in the pharmaceutical industry. This blog seeks to demystify the application of AI in drug discovery, focusing on its key challenges, opportunities and successes. Over one million scientific articles are published every year in the biomedical domain alone, and every new year brings new methods for data collection and more detailed data modalities. While scientists have access to an exponentially increasing amount of knowledge and data, biological data is messy and incomplete; it may contain conflicting or contradicting evidence, suppositions, biases, uncertainty, gaps in knowledge or misclassifications. This prevents us from understanding the full biology landscape and complicates decision making.

Drug discovery and development is an incredibly important yet capital intensive, lengthy and low efficiency process. In recent years, biology and chemistry has become increasingly high-throughput and data-driven thanks to massively parallel sequencing, robotic liquid handling robots, advanced imaging techniques and more. This has opened up the opportunity for machine learning techniques to not only improve experimental analysis but also to generate novel experimental hypotheses that are worth testing. For example, machine learning models can be used in virtual screens where they generate candidate molecules that are likely to have a desired phenotypic effect. While the number of virtual screens is increasing, there are fewer studies that close the loop with empirical results.

After returning from summer vacation in September 1928, bacteriologist Alexander Fleming found a colony of bacteria he'd left in his London lab had sprouted a fungus. Curiously, wherever the bacteria contacted the fungus, their cell walls broke down and they died. Fleming guessed the fungus was secreting something lethal to the bacteria--and the rest is history. Fleming's discovery of penicillin and its later isolation, synthesis, and scaling in the 1940s released a flood of antibiotic discoveries in the next few decades. Bacteria and fungi had been waging an ancient war against each other, and the weapons they'd evolved over eons turned out to be humanity's best defense against bacterial infection and disease.