Some computational models are combined with AI models to predict absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug molecules. Lipophilicity and solubility of the compounds can be predicted using neural network programs such as ADMET predictor and the ALGOPS program. This helps in the optimisation of lead molecules. Predicting in vivo activity and toxicity of molecules is important in identifying the pre-clinical candidate molecules. Algorithms, such as extreme learning machines and Deep Neural Networks (DNN) were developed for this purpose. ³, ⁴

De novo drug design is also made possible through computer-aided synthesis planning programs like Synthia, which effectively proposes possible synthesising routes or alternate synthesising strategies for patented compounds. ⁵ The involvement of AI in the de novo design of molecules can be beneficial to the pharmaceutical sector, leading to swift lead design and development compared to traditional methods. De novo drug design has two models: the Generative Model that generates unique molecules and the Predictive model that evaluates the properties of the generated molecules.

Many leading pharmaceutical companies like Roche, Pfizer, Novartis and Astrazeneca have collaborated with, and continue to do so, with AI organisations in various fields including Oncology, Cardiovascular diseases, and CNS disorders. ⁶