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Optimizing reaction conditions depends on expert chemistry knowledge and laborious exploration of reaction parameters. To automate this task and augment chemical intuition, we here report a computational tool to navigate search spaces. Our approach (LabMate.ML) integrates random sampling of 0.03%–0.04% of all search space as input data with an interpretable, adaptive machine-learning algorithm. LabMate.ML can optimize many real-valued and categorical reaction parameters simultaneously, with minimal computational resources and time. In nine prospective proof-of-concept studies pursuing distinctive objectives, we demonstrate how LabMate.ML can identify optimal goal-oriented conditions for several different chemistries and substrates. Double-blind competitions and the conducted expert surveys reveal that its performance is competitive with that of human experts. LabMate.ML does not require specialized hardware, affords quantitative and interpretable reactivity insights, and autonomously formalizes chemical intuition, thereby providing an innovative framework for informed, automated experiment selection toward the democratization of synthetic chemistry. ; D.R. is a Swiss National Science Foundation Fellow (grant nos. P2EZP3_168827 and P300P2_177833). E.A.H. is supported by the Herchel Smith Fellowship awarded by Williams College. G.J.L.B. is a Royal Society URF (URF\R\180019). T.R. is an Investigador Auxiliar supported by FCT Portugal (CEECIND/00887/2017). T.R. acknowledges the H2020 (TWINN-2017 ACORN, grant no. 807281), FCT/FEDER (02/SAICT/2017, grant no. 28333). D.R. acknowledges the MIT-IBM Watson AI Lab and the MIT SenseTime coalition for funding. The authors are extremely grateful to several colleagues for suggesting Ugi reaction conditions, and to Prof. R. Langer and Prof. G. Traverso, who provided invaluable comments on the research and manuscript. The authors are indebted to Prof. R. Moreira for access to the CEM microwave reactor; Dr. F. Corzana for technical assistance with HRMS; and the 13 graduate ...

Copyright © 2022 The Authors. Published by American Chemical Society ; Antibody-drug conjugates (ADCs) are a class of targeted therapeutics used to selectively kill cancer cells. It is important that they remain intact in the bloodstream and release their payload in the target cancer cell for maximum efficacy and minimum toxicity. The development of effective ADCs requires the study of factors that can alter the stability of these therapeutics at the atomic level. Here, we present a general strategy that combines synthesis, bioconjugation, linker technology, site-directed mutagenesis, and modeling to investigate the influence of the site and microenvironment of the trastuzumab antibody on the stability of the conjugation and linkers. Trastuzumab is widely used to produce targeted ADCs because it can target with high specificity a receptor that is overexpressed in certain breast cancer cells (HER2). We show that the chemical environment of the conjugation site of trastuzumab plays a key role in the stability of linkers featuring acid-sensitive groups such as acetals. More specifically, Lys-207, located near the reactive Cys-205 of a thiomab variant of the antibody, may act as an acid catalyst and promote the hydrolysis of acetals. Mutation of Lys-207 into an alanine or using a longer linker that separates this residue from the acetal group stabilizes the conjugates. Analogously, Lys-207 promotes the beneficial hydrolysis of the succinimide ring when maleimide reagents are used for conjugation, thus stabilizing the subsequent ADCs by impairing the undesired retro-Michael reactions. This work provides new insights for the design of novel ADCs with improved stability properties. ; We thank the Agencia Estatal Investigación of Spain (AEI; Grants RTI2018-099592-B-C21 to F.C. and RTI2018-099592-B-C22 to G.J.-O.). This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 675007. E.J.-M. thanks Universidad de la Rioja for ...