Protein ubiquitination and deubiquitination are central to the control of a large number of cellular pathways and signaling networks in eukaryotes. Although the essential roles of ubiquitination have been established in the eukaryotic DNA damage response, the deubiquitination process remains poorly defined. Chemical probes that perturb the activity of deubiquitinases (DUBs) are needed to characterize the cellular function of deubiquitination. Here we report ML323 (2), a highly potent inhibitor of the USP1-UAF1 deubiquitinase complex with excellent selectivity against human DUBs, deSUMOylase, deneddylase and unrelated proteases. Using ML323, we interrogated deubiquitination in the cellular response to UV- and cisplatin-induced DNA damage and revealed new insights into the requirement of deubiquitination in the DNA translesion synthesis and Fanconi anemia pathways. Moreover, ML323 potentiates cisplatin cytotoxicity in non-small cell lung cancer and osteosarcoma cells. Our findings point to USP1-UAF1 as a key regulator of the DNA damage response and a target for overcoming resistance to the platinum-based anticancer drugs.

The general goal of drug discovery is to identify novel compounds that are active against a preselected biological target with acceptable pharmacological properties defined by marketed drugs. Scaffold hopping has been widely applied by medicinal chemists to discover equipotent compounds with novel backbones that have improved properties. In this review, scaffold hopping is classified into four major categories, namely heterocycle replacements, ring opening or closure, peptidomimetics, and topology-based hopping. The structural diversity of original and final scaffolds with respect to each category will be reviewed. The advantages and limitations of small, medium, and large-step scaffold hopping will also be discussed. Software that is frequently used to facilitate different kinds of scaffold hopping methods will be summarized.

Predictive models for octanol/water partition coefficient (logP), aqueous solubility (logS), blood-brain barrier (logBB), and human intestinal absorption (HIA) were built from a universal, generic molecular descriptor system, designed on the basis of atom type classification. The atom type classification tree was trained to optimize the logP predictions. With nine components, the final partial least-squares (PLS) model predicted logP of 10850 compounds in Starlist with a regression coefficient (r2) of 0.912, cross-validated r2 (q2) of 0.892, and root-mean-square error of estimation (RMSEE) of 0.50 log units. The PLS models for solubility (logS), blood-brain barrier (logBB), and a PLS-DA (discrimination analysis) model for HIA were established from the same atom type descriptors. The seven-component PLS model derived from a diverse set of 1478 organic compounds predicted a 21-compound test set designed by Yalkowsky with r2 = 0.88 and RMSEP (RMS error of prediction) = 0.64. A predictive r2 = 0.90 and RMSEE = 0.26 were achieved for logBB of a 57-compound "Abraham data set" with a three-component model. The first three components of a five-component PLS-DA model were sufficient to clearly separate the 169 drug molecules, collected by Abraham, into three classes, according to their percentage human intestinal absorption.