In addition to in vitro and in vivo methods, we can use in silico methods to mitigate serendipity and rationalize those phenomena that experimental methods cannot explain. During rational drug design, serendipity might occur, leading to unexpected but potentially positive results such as the discovery of Lyrica (pregabalin). However, the dramatic increase of data in chemical databases with biological annotations limits the chance of serendipitous positive results and calls for enhanced methods for the identification of molecules with clinical application. Such tasks can be addressed by using AI. Increasing numbers of AI algorithms are being developed for predicting the relationship between chemical structure and biological activity. For example, DeepChem is an open-source platform with tools for applying AI algorithms that allow the prediction not only of biological activity but also of multiple drug properties, including physicochemical features, and toxicity. DeepChem has supported the development and benchmarking of new AI models with diverse goals. Efficacy prediction using diverse inputs (chemical and physicochemical properties, biological -in vitro and in vivo-information, -omics, preclinical, clinical, and post-marketing data) is one of the main objectives of applying AI in drug design. For example, Wang et al. used a model based on Support Vector Machines (SVMs) to discover nine new compounds and their interactions with four key targets. The model was trained on 15,000 protein-ligand interactions and was developed based on primary protein sequences and structural characteristics of small molecules. Yu et al. used two random forests (RF) models with high sensitivity and specificity to predict possible drug-protein interactions by combining pharmacological and chemical data. KinomeX, an AI-based online platform trained with over ~14 000 bioactivity data points derived from over ~300 kinases, can efficiently investigate the overall selectivity of compounds for the kinase family and specific subfamilies of kinases, which can aid in developing new chemical modifiers. A last example is PyRMD, an AI algorithm based in Random Matrix Discriminant (MD) -a subtype of ML-that can be trained to recognize the distinctive pharmacophoric features from the target bioactivity data available at the ChEMBL. Selected negative data are incorporated in the learning to identify structural features that are irrelevant or detrimental for the intended bioactivity. Identification of toxic effects in early stages of drug design allows to remove undesirable characteristics of bioactive compounds. At present, multiple AI-based are employed to assess toxicity by predicting the offtarget ligand binding. For example, Ligand Express, Cyclica's cloud-based AI platform, uses proteomescreening data to find receptors that can interact with a specific small molecule, predicting on-and off-target interactions and suggesting the drug's potential side effects. Other AI web-based tools that help predict toxicity include LimTox, pkCSM, admetSAR, and Toxtree. A particularly remarkable case is DeepTox, an ML-based algorithm that using features within chemical descriptors accurately predicted the toxicity of 12,707 environmental compounds and drugs during the Tox21 Data Challenge. The DeepTox algorithm uses static descriptors, such as molecular weight, Van der Waals volume, or the presence/ absence of a predefined substructure or a toxicophore descriptor, as well as calculated dynamic descriptors. Despite a potentially infinite number of different dynamic features, the method keeps the dataset within manageable limits and shows good accuracy in predicting the toxicology of compounds.