How fragments are optimized? A retrospective analysis of 145 fragment optimizations.

Fragment optimizations in nearly 150 fragment-based drug discovery programs reported in the literature during the last fifteen years were investigated. Analyzing physico-chemical properties and ligand efficiency indices we found that biochemical detection methods yield hits with superior ligand efficiency and lipophilicity indices than do X-ray and NMR. These advantageous properties are partially preserved in the optimization since higher affinity starting points allow optimizations better balanced between affinity and physico-chemical property improvements. Size-independent ligand efficiency (SILE) and lipophilic indices (primarily LELP) were found to be appropriate metrics to monitor optimizations. Small and medium enterprises (SME) produce optimized compounds with better properties than do big pharma companies and universities. It appears that the use of target structural information is a major reason behind this finding. Structure-based optimization was also found to dominate successful fragment optimizations that result in clinical candidates. These observations provide optimization guidelines for fragment-based drug discovery programs

Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia

Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function; it can cover broad swaths of chemical space and allows the use of creative chemistry. FBDD is widely implemented for lead discovery in industry but is sometimes. used less systematically in academia. Design principles and implementation approaches for fragment libraries are continually evolving, and the lack of up-to-date guidance may prevent more effective application of FBDD in academia. This Perspective explores many of the theoretical, practical, and strategic considerations that occur within FBDD programs, including the optimal size, complexity, physicochemical profile, and shape profile of fragments in FBDD libraries, as well as compound storage, evaluation; and screening technologies. This:compilation of industry experience in FBDD will hopefully be useful for those pursuing FBDD in academia

The role of quantum chemistry in covalent inhibitor design

The recent ascent of targeted covalent inhibitors (TCI) in drug discovery brings new opportunities and challenges to quantum chemical reactivity calculations supporting discovery efforts. TCIs typically form a covalent bond with the targeted nucleophilic amino acid side chain. Their reactivity that can be both computed and experimentally measured is therefore one of the key factors in determining inhibitory potency. Calculation of relevant quantum chemical descriptors and corresponding reaction barriers of model reactions represent efficient ways to predict intrinsic reactivities of covalent ligands. A more comprehensive description of covalent ligand binding is offered by mixed quantum mechanical/molecular mechanical (QM/MM) potentials. Reaction mechanisms can be investigated by the exploration of the potential energy surface as a function of suitable reaction coordinates, and free energy surfaces can also be calculated with molecular dynamics based simulations. Here we review the methodological aspects and discuss applications with primary focus on high-end QM/MM simulations to illustrate the current status of quantum chemical support to covalent inhibitor design. Available QM approaches are suitable to identify likely reaction mechanisms and rate determining steps in the binding of covalent inhibitors. The efficient QM/MM prediction of ligand reactivities complemented with the computational description of the recognition step makes these computations highly useful in covalent drug discovery