KID: A Kinase-Focused Interaction Database and Its Application in the Construction of Kinase-Focused Molecule Databases

Protein kinases are important drug targets for the treatment of several diseases. The interaction between kinases and ligands is vital in the process of small-molecule kinase inhibitor (SMKI) design. In this study, we propose a method to extract fragments and amino acid residues from crystal structures for kinase-ligand interactions. In addition, core fragments that interact with the important hinge region of kinases were extracted along with their decorations. Based on the superimposed structural data of kinases from the kinase-ligand interaction fingerprint and structure database, we obtained two libraries, namely, a hinge-unfocused fragment-amino acid pair library (FAP Lib) that contains 6672 pairs of fragments and corresponding amino-acids, and a hinge-focused hinge binder library (HB Lib) of 3560 pairs of hinge-binding scaffolds with their corresponding decorations. These two libraries constitute a kinase-focused interaction database (KID). In depth analysis was conducted on KID to explore important characteristics of fragments in the design of SMKIs. With KID, we built two kinase-focused molecule databases, one called Recomb_DB, which contains 1,72,346 molecules generated through fragment recombination based on the FAP Lib, and another called RsdHB_DB, which contains 93,030 molecules generated based on our HB Lib using molecular generation methods. Compared with five databases both commercial and non-commercial, these two databases both ranked top 3 in scaffold diversity, top 4 in molecule fingerprint diversity, and are more focused on the chemical space of kinase inhibitors. Hence, KID presents a useful addition to existing databases for the exploration of novel SMKIs.

KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination

Protein kinases play a crucial role in many cell signaling processes, making them one of the most important families of drug targets. In this context, fragment-based drug design strategies have been successfully applied to develop novel kinase inhibitors. These strategies usually follow a knowledge-driven approach to optimize a focused set of fragments to a potent kinase inhibitor. Alternatively, KinFragLib explores and extends the chemical space of kinase inhibitors using data-driven fragmentation and recombination. The method builds on available structural kinome data from the KLIFS database for over 2500 kinase DFG-in structures cocrystallized with noncovalent kinase ligands. The computational fragmentation method splits the ligands into fragments with respect to their 3D proximity to six predefined functionally relevant subpocket centers. The resulting fragment library consists of six subpocket pools with over 7000 fragments, available at https://github.com/volkamerlab/KinFragLib. KinFragLib offers two main applications: on the one hand, in-depth analyses of the chemical space of known kinase inhibitors, subpocket characteristics, and connections, and on the other hand, subpocket-informed recombination of fragments to generate potential novel inhibitors. The latter showed that recombining only a subset of 624 representative fragments generated 6.7 million molecules. This combinatorial library contains, besides some known kinase inhibitors, more than 99% novel chemical matter compared to ChEMBL and 63% molecules compliant with Lipinski's rule of five.

Current Anti-Inflammatory Therapies and the Potential of Secretory Phospholipase A2 Inhibitors in the Design of New Anti-Inflammatory Drugs: A Review of 2012 - 2018

The inflammatory process is a natural self-defense response of the organism to damage agents and its action mechanism involves a series of complex reactions. However, in some cases, this process can become chronic, causing much harm to the body. Therefore, over the years, many anti-inflammatory drugs have been developed aiming to decrease the concentrations of inflammatory mediators in the organism, which is a way of controlling these abnormal chain reactions. The main target of conventional anti-inflammatory drugs is the cyclooxygenase (COX) enzyme, but its use implies several side effects. Thus, based on these limitations, many studies have been performed, aiming to create new drugs, with new action mechanisms. In this sense, the phospholipase A2 (PLA2) enzymes stand out. Among all the existing isoforms, secretory PLA2 is the major target for inhibitor development, since many studies have proven that this enzyme participates in various inflammatory conditions, such as cancer, Alzheimer and arthritis. Finally, for the purpose of developing anti-inflammatory drugs that are sPLA2 inhibitors, many molecules have been designed. Accordingly, this work presents an overview of inflammatory processes and mediators, the current available anti-inflammatory drugs, and it briefly covers the PLA2 enzymes, as well as the diverse structural array of the newest sPLA2 inhibitors as a possible target for the production of new anti-inflammatory drugs.

Non-competitive inhibitor of nucleoside hydrolase from Leishmania donovani identified by fragment-based drug discovery

New non-competitive inhibitor of nucleoside hidrolase fromL. donovaniidentified by fragment-based drug discovery using STD NMR and molecular docking.

Evolutions in fragment-based drug design: the deconstruction-reconstruction approach

Recent advances in the understanding of molecular recognition and protein-ligand interactions have facilitated rapid development of potent and selective ligands for therapeutically relevant targets. Over the past two decades, a variety of useful approaches and emerging techniques have been developed to promote the identification and optimization of leads that have high potential for generating new therapeutic agents. Intriguingly, the innovation of a fragment-based drug design (FBDD) approach has enabled rapid and efficient progress in drug discovery. In this critical review, we focus on the construction of fragment libraries and the advantages and disadvantages of various fragment-based screening (FBS) for constructing such libraries. We also highlight the deconstruction-reconstruction strategy by utilizing privileged fragments of reported ligands.

Site Identification by Ligand Competitive Saturation (SILCS) simulations for fragment-based drug design

Fragment-based drug design (FBDD) involves screening low molecular weight molecules ("fragments") that correspond to functional groups found in larger drug-like molecules to determine their binding to target proteins or nucleic acids. Based on the principle of thermodynamic additivity, two fragments that bind nonoverlapping nearby sites on the target can be combined to yield a new molecule whose binding free energy is the sum of those of the fragments. Experimental FBDD approaches, like NMR and X-ray crystallography, have proven very useful but can be expensive in terms of time, materials, and labor. Accordingly, a variety of computational FBDD approaches have been developed that provide different levels of detail and accuracy.The Site Identification by Ligand Competitive Saturation (SILCS) method of computational FBDD uses all-atom explicit-solvent molecular dynamics (MD) simulations to identify fragment binding. The target is "soaked" in an aqueous solution with multiple fragments having different identities. The resulting computational competition assay reveals what small molecule types are most likely to bind which regions of the target. From SILCS simulations, 3D probability maps of fragment binding called "FragMaps" can be produced. Based on the probabilities relative to bulk, SILCS FragMaps can be used to determine "Grid Free Energies (GFEs)," which provide per-atom contributions to fragment binding affinities. For essentially no additional computational overhead relative to the production of the FragMaps, GFEs can be used to compute Ligand Grid Free Energies (LGFEs) for arbitrarily complex molecules, and these LGFEs can be used to rank-order the molecules in accordance with binding affinities.

Vemurafenib in melanoma

The incidence of malignant melanoma is increasing annually. Early stages can be cured with surgical intervention but metastatic disease has generally had a dismal prognosis with few effective interventions. A half of all melanomas possess a BRAF mutation, which can be targeted by specific inhibitors. Vemurafenib is an orally active, purposely designed mutant BRAF inhibitor, which has recently been shown to have a survival benefit measured in months in metastatic patients. In this article, the authors discuss the scientific rationale, drug development process and clinical trials that have led to vemurafenib becoming the first BRAF inhibitor approved for the treatment of patients with mutant BRAF metastatic melanoma.

Exploiting structural information in patent specifications for key compound prediction

Patent specifications are one of many information sources needed to progress drug discovery projects. Understanding compound prior art and novelty checking, validation of biological assays, and identification of new starting points for chemical explorations are a few areas where patent analysis is an important component. Cheminformatics methods can be used to facilitate the identification of so-called key compounds in patent specifications. Such methods, relying on structural information extracted from documents by expert curation or text mining, can complement or in some cases replace the traditional manual approach of searching for clues in the text. This paper describes and compares three different methods for the automatic prediction of key compounds in patent specifications using structural information alone. For this data set, the cluster seed analysis described by Hattori et al. (Hattori, K.; Wakabayashi, H.; Tamaki, K. Predicting key example compounds in competitors' patent applications using structural information alone. J. Chem. Inf. Model.2008, 48, 135-142) is superior in terms of prediction accuracy with 26 out of 48 drugs (54%) correctly predicted from their corresponding patents. Nevertheless, the two new methods, based on frequency of R-groups (FOG) and maximum common substructure (MCS) similarity measures, show significant advantages due to their inherent ability to visualize relevant structural features. The results of the FOG method can be enhanced by manual selection of the scaffolds used in the analysis. Finally, a successful example of applying FOG analysis for designing potent ATP-competitive AXL kinase inhibitors with improved properties is described.

Medicinal chemistry for 2020

Rapid advances in our collective understanding of biomolecular structure and, in concert, of biochemical systems, coupled with developments in computational methods, have massively impacted the field of medicinal chemistry over the past two decades, with even greater changes appearing on the horizon. In this perspective, we endeavor to profile some of the most prominent determinants of change and speculate as to further evolution that may consequently occur during the next decade. The five main angles to be addressed are: protein-protein interactions; peptides and peptidomimetics; molecular diversity and pharmacological space; molecular pharmacodynamics (significance, potential and challenges); and early-stage clinical efficacy and safety. We then consider, in light of these, the future of medicinal chemistry and the educational preparation that will be required for future medicinal chemists.