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Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.

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A NiH-catalyzed migratory hydroalkylation of alkenyl amines with predictable and switchable regioselectivity is reported. By utilizing a ligand-controlled, directing group-assisted strategy, various alkyl units are site-selectively installed at inert sp 3 CÀ H sites far away from the original C=C bonds. A range of structurally diverse α-and β-branched protected amines are conveniently synthesized via stabilization of 5-and 6-membered nickelacycles respectively. This method exhibits broad scope and high functional group tolerance, and can be applied to late-stage modification of medicinally relevant molecules.