Machine learning (ML) refers to data analysis tools that can extract dependencies from the data without being explicitly programmed, thereby providing an attractive alternative to other approaches in areas where few or no prior data are available about those dependencies or where they are too complex.

Deep machine learning or deep learning (DL) comprises a set of methods that rely on deep architectures with cascades of multiple layers, and include architectures such as deep neural networks (DNN), generative adversarial networks (GAN), deep reinforcement learning, and others.

DNNs are models with multiple hidden layers between the input and output layers. The multilinearity of DNNs combined with non-linear activation functions provides them with exceptional ability to extract complex dependencies in the data and automatically select features that are most relevant to predictions. In the case of the age prediction, networks are trained using biological data as the input to predict age as accurately as possible.

GANs are a type of a DL model that comprises discriminator and generator networks. A generator produces a candidate vector of synthetic data, and a discriminator networks check the vector validity. Such data generation has been extensively explored for new pharmacological agents, and can also be used to generate synthetic data for patients.

Reinforcement learning (RL) is a type of goal-oriented algorithm that is trained to attain a complex objective over many steps. In case of drug discovery, such an objective could include the drug-likeness of molecules, their ease of synthesis, and other desired properties. RL algorithms could also be deep and have a multilayered architecture.