01719nas a2200193   4500000000100000008004100001260001000042653003900052653002400091653001700115653003000132653003400162100002300196700001400219700002200233245011300255520113200368022002501500       2021                            d  bWiley10aPhysical and Theoretical Chemistry10aMaterials Chemistry10aBiochemistry10aComputational Mathematics10aComputer Science Applications1 aPeña‐Guerrero J1 aNguewa PA1 aGarcía‐Sosa AT00aMachine learning, artificial intelligence, and data science breaking into drug design and neglected diseases3 aMachine learning (ML) is becoming capable of transforming biomolecular interaction description and calculation, promising an impact on molecular and drug design, chemical biology, toxicology, among others. The first improvements can be seen from biomolecule structure prediction to chemical synthesis, molecular generation, mechanism of action elucidation, inverse design, polypharmacology, organ or issue targeting of compounds, property and multiobjective optimization. Chemical design proposals from an algorithm may be inventive and feasible. Challenges remain, with the availability, diversity, and quality of data being critical for developing useful ML models; marginal improvement seen in some cases, as well as in the interpretability, validation, and reuse of models. The ultimate aim of ML should be to facilitate options for the scientist to propose and undertake ideas and for these to proceed faster. Applications are ripe for transformative results in understudied, neglected, and rare diseases, where new data and therapies are strongly required. Progress and outlook on these themes are provided in this study.  a1759-0876, 1759-0884