Guo-Bo Li

ID-Score: a new empirical scoring function based on a comprehensive set of descriptors related to protein-ligand interactions.

Scoring functions have been widely used to assess protein-ligand binding affinity in structure-based drug discovery. However, currently commonly used scoring functions face some challenges including poor correlation between calculated scores and experimental binding affinities, target-dependent performance, and low sensitivity to analogues. In this account, we propose a new empirical scoring function termed ID-Score. ID-Score was established based on a comprehensive set of descriptors related to protein-ligand interactions; these descriptors cover nine categories: van der Waals interaction, hydrogen-bonding interaction, electrostatic interaction, π-system interaction, metal-ligand bonding interaction, desolvation effect, entropic loss effect, shape matching, and surface property matching. A total of 2278 complexes were used as the training set, and a modified support vector regression (SVR) algorithm was used to fit the experimental binding affinities. Evaluation results showed that ID-Score outperformed other selected commonly used scoring functions on a benchmark test set and showed considerable performance on a large independent test set. ID-Score also showed a consistent higher performance across different biological targets. Besides, it could correctly differentiate structurally similar ligands, indicating higher sensitivity to analogues. Collectively, the better performance of ID-Score enables it as a useful tool in assessing protein-ligand binding affinity in structure-based drug discovery as well as in lead optimization.

Structure–Activity Relationship Studies of Pyrazolo[3,4-d]pyrimidine Derivatives Leading to the Discovery of a Novel Multikinase Inhibitor That Potently Inhibits FLT3 and VEGFR2 and Evaluation of Its Activity against Acute Myeloid Leukemia in Vitro and in Vivo

We describe the structural optimization of a hit compound, 1-(4-(1H-pyrazolo[3,4-d]pyrimidin-4-ylamino)phenyl)-3-(3-methoxyphenyl)urea (1), which exhibits inhibitory activity but low potency against FLT3 and VEGFR2. A series of pyrazolo[3,4-d]pyrimidine derivatives were synthesized, and structure-activity relationship analysis using cell- and transgenic-zebrafish-based assays led to the discovery of a number of compounds that exhibited both high potency against FLT3-driven human acute myeloid leukemia (AML) MV4-11 cells and a considerable antiangiogenic effect in transgenic-zebrafish-based assays. The compound 1-(4-(1H-pyrazolo[3,4-d]pyrimidin-4-yloxy)phenyl)-3-(4-chloro-3-(trifluoromethyl)phenyl)urea (33), which exhibited the highest activity in preliminary in vivo anti-AML assays, was chosen for further anti-AML studies. The results demonstrated that compound 33 is a multikinase inhibitor that potently inhibits FLT3 and VEGFR2. In an MV4-11 xenograft mouse model, a once-daily dose of compound 33 at 10 mg/kg for 18 days led to complete tumor regression without obvious toxicity. Western blot and immunohistochemical analyses were performed to determine the mechanism of action of compound 33.

Discovery of N6-phenyl-1H-pyrazolo[3,4-d]pyrimidine-3,6-diamine derivatives as novel CK1 inhibitors using common-feature pharmacophore model based virtual screening and hit-to-lead optimization

Aberrant activation of casein kinase 1 (CK1) has been demonstrated to be implicated in the pathogenesis of cancer and various central nervous system disorders. Discovery of CK1 inhibitors has thus attracted much attention in recent years. In this account, we describe the discovery of N6-phenyl-1H-pyrazolo[3,4-d]pyrimidine-3,6-diamine derivatives as novel CK1 inhibitors. An optimal common-feature pharmacophore hypothesis, termed Hypo2, was firstly generated, followed by virtual screening using Hypo2 against several chemical databases. One of the best hit compounds, N6-(4-chlorophenyl)-1H-pyrazolo[3,4-d]pyrimidine-3,6-diamine, was chosen for the subsequent hit-to-lead optimization under the guide of Hypo2, which led to the discovery of a new lead compound (1-(3-(3-amino-1H-pyrazolo[3,4-d]pyrimidin-6-ylamino)phenyl)-3-(3-chloro-4-fluorophenyl)urea) that potently inhibits CK1 with an IC(50) value of 78 nM.

Design, Synthesis, and Structure–Activity Relationship Studies of 3-(Phenylethynyl)-1H-pyrazolo[3,4-d]pyrimidin-4-amine Derivatives as a New Class of Src Inhibitors with Potent Activities in Models of Triple Negative Breast Cancer

A series of 3-(phenylethynyl)-1H-pyrazolo[3,4-d]pyrimidin-4-amine derivatives were designed and synthesized. Structure-activity relationship (SAR) analysis of these compounds led to the discovery of compound 1j, which showed the highest inhibitory potency against the Src kinase and the most potent antiviability activity against the typical TNBC cell line MDA-MB-231 among all the synthesized compounds. Further kinase inhibition assays showed that compound 1j was a multikinase inhibitor and potently inhibited Src (IC50 = 0.0009 μM) and MAPK signaling protein kinases B-RAF and C-RAF. In an MDA-MB-231 xenograft mouse model, a once-daily dose of compound 1j at 30 mg/kg for 18 days completely suppressed the tumor growth with a tumor inhibition rate larger than 100% without obvious toxicity. It also displayed good pharmacokinetic properties in a preliminary pharmacokinetic assay. Western blot and immunohistochemical assays revealed that compound 1j significantly inhibited Src and MAPK signaling and markedly induced apoptosis in tumor tissues.

Virtual screening in small molecule discovery for epigenetic targets.

Epigenetic modifications are critical mechanisms that regulate many biological processes and establish normal cellular phenotypes. Aberrant epigenetic modifications are frequently linked to the development and maintenance of several diseases including cancer, inflammation and metabolic diseases and so on. The key proteins that mediate epigenetic modifications have been thus recognized as potential therapeutic targets for these diseases. Consequently, discovery of small molecule inhibitors for epigenetic targets has received considerable attention in recent years. Here, virtual screening methods and their applications in the discovery of epigenetic target inhibitors are the focus of this review. Newly emerging approaches or strategies including rescoring methods, docking pose filtering methods, machine learning methods and 3D molecular similarity methods were also underlined. They are expected to be employed for identifying novel inhibitors targeting epigenetic regulation more efficiently.

A combined molecular docking-based and pharmacophore-based target prediction strategy with a probabilistic fusion method for target ranking.

Herein, a combined molecular docking-based and pharmacophore-based target prediction strategy is presented, in which a probabilistic fusion method is suggested for target ranking. Establishment and validation of the combined strategy are described. A target database, termed TargetDB, was firstly constructed, which contains 1105 drug targets. Based on TargetDB, the molecular docking-based target prediction and pharmacophore-based target prediction protocols were established. A probabilistic fusion method was then developed by constructing probability assignment curves (PACs) against a set of selected targets. Finally the workflow for the combined molecular docking-based and pharmacophore-based target prediction strategy was established. Evaluations of the performance of the combined strategy were carried out against a set of structurally different single-target compounds and a well-known multi-target drug, 4H-tamoxifen, which results showed that the combined strategy consistently outperformed the sole use of docking-based and pharmacophore-based methods. Overall, this investigation provides a possible way for improving the accuracy of in silico target prediction and a method for target ranking.

LEADOPT: an automatic tool for structure-based lead optimization, and its application in structural optimizations of VEGFR2 and SYK inhibitors.

Lead optimization is one of the key steps in drug discovery, and currently it is carried out mostly based on experiences of medicinal chemists, which often suffers from low efficiency. In silico methods are thought to be useful in improving the efficiency of lead optimization. Here we describe a new in silico automatic tool for structure-based lead optimization, termed LEADOPT. The structural modifications in LEADOPT mainly include two operations: fragment growing and fragment replacing, which are restricted to carry out in the active pocket of target protein with the core scaffold structure of ligand kept unchanged. The bioactivity of the newly generated molecules is estimated by ligand efficiency rather than a commonly used scoring function. Twelve important pharmacokinetic and toxic properties are evaluated using SCADMET, a program for the prediction of pharmacokinetic and toxic properties. LEADOPT was first evaluated using two retrospective cases, in which it showed a very good performance. LEADOPT was then applied to the structural optimizations of the VEGFR2 inhibitor, sorafenib, and the SYK inhibitor, R406. Though just several compounds were synthesized, we have obtained some compounds that are more potent than sorafenib and R406 in enzymatic and functional assays. All of these have validated, at least to some extent, the effectiveness of LEADOPT.

Drug Discovery against Psoriasis: Identification of a New Potent FMS-like Tyrosine Kinase 3 (FLT3) Inhibitor, 1-(4-((1H-Pyrazolo[3,4-d]pyrimidin-4-yl)oxy)-3-fluorophenyl)-3-(5-(tert-butyl)isoxazol-3-yl)urea, That Showed Potent Activity in a Psoriatic Animal Model

Psoriasis is a chronic T-cell-mediated autoimmune disease, and FMS-like tyrosine kinase 3 (FLT3) has been considered as a potential molecular target for the treatment of psoriasis. In this investigation, structural optimization was performed on a lead compound, 1-(4-(1H-pyrazolo[3,4-d]pyrimidin-4-yloxy)phenyl)-3-(4-chloro-3-(trifluoromethyl)phenyl)urea (1), which showed a moderate inhibitory activity againt FLT3. A series of pyrazolo[3,4-d]pyrimidine derivatives were synthesized, and structure-activity relationship analysis led to the discovery of a number of potent FLT3 inhibitors. One of the most active compounds, 1-(4-(1H-pyrazolo[3,4-d]pyrimidin-4-yloxy)-3-fluorophenyl)-3-(5-tert-butylisoxazol-3-yl)urea (18b), was then chosen for in-depth antipsoriasis studies because this compound displayed the highest potency in a preliminary antipsoriasis test. Compound 18b exhibited significant antipsoriatic effects in the K14-VEGF transgenic mouse model of psoriasis, and no recurrence was found 15 days later after the last administration. Detailed mechanisms of action of compound 18b were also investigated. Collectively, compound 18b could be a potential drug candidate for psoriasis treatment.

A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery.

Sirtuin 5: a review of structure, known inhibitors and clues for developing new inhibitors

Sirtuins (SIRTs) are nicotinamide adenine dinucleotide (NAD+)-dependent protein deacetylases, which regulate important biological processes ranging from apoptosis, age-associated pathophysiologies, adipocyte and muscle differentiation, and energy expenditure to gluconeogenesis. Very recently, sirtuin 5 (SIRT5) has received considerable attention due to that it was found to have weak deacetylase activity but strong desuccinylase, demalonylase and deglutarylase activities, and it was also found to be associated with several human diseases such as cancer, Alzheimer’s disease, and Parkinson’s disease. In this review, we for the first time summarized the structure characteristics, known peptide and smallmolecule inhibitors of SIRT5, extracted some clues from current available information and introduced some feasible, practical in silico methods, which might be useful in further efforts to develop new SIRT5 inhibitors.