Insilico claims major victory as AI outperforms big pharma in early drug R&D

Hong Kong’s Insilico Medicine has found a way to use AI and deep learning to design, synthesise and validate a novel drug candidate in 46 days – 15 times faster than the best pharma companies. A new scientific paper published in Nature Biotechnology said that the company used cutting-edge AI and deep learning techniques of Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) for drug discovery and biomarker development. Insilico Medicine was the first to use GANs to generate novel molecules in 2016 and since then has spent two years developing ways to combine the techniques for drug development. These efforts have now been used to design a novel DDR1 kinase inhibitor from scratch in 21 days, then to synthesise and pre-clinically validate it in 25 days. AI is increasingly being used in drug R&D, with companies such as BenevolentAI, Exscientia in the UK, and US-based Google DeepMind and Berg becoming involved. GANs are specialised algorithms that create synthetic datasets that are indistinguishable from real datasets, by having two neural networks that compete against each other. One neural network generates the data and the other compares it to a real data set in iterative cycles so that the degree of error in the synthetic data set is gradually decreased.