Kinase Crystal Miner: A Powerful Approach to Repurposing 3D Hinge Binding Fragments and Its Application to Finding Novel Bruton Tyrosine Kinase Inhibitors

Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.

How Ligands Interact with the Kinase Hinge

ATP-competitive kinase inhibitors form hydrogen bond interactions with the kinase hinge region at the adenine binding site. Thus, it is crucial to explore hinge-ligand recognition as part of a rational drug design strategy. Here, harnessing known ligand-bound kinase structures and experimental assay resources, we first created a kinase structure-assay database (KSAD) containing 2705 nM ligand-bound kinase complexes. Then, using KSAD, we systematically investigate hinge-ligand binding patterns using interaction fingerprints, thereby delineating 15 different hydrogen-bond interaction modes. We believe these results will be valuable for de novo drug design and/or scaffold hopping of kinase-targeted drugs.

Application of an "inhalation by design" approach to the identification and in-vitro evaluation of novel purine based PI3Kδ inhibitors

PI3Kδ is a lipid kinase which plays a key role in airway inflammatory conditions. Accordingly, the inhibition of PI3Kδ can be considered a valuable strategy for the treatment of chronic respiratory diseases such as Asthma and Chronic obstructive pulmonary disease (COPD). In this work, we describe our efforts to identify new PI3Kδ inhibitors following an "inhalation by design" strategy. Starting from the identification of a purine scaffold, we carried out a preliminary SAR expansion which led to the identification of a new hit characterized by a high enzymatic potency and moderate PI3Kδ selectivity. A subsequent optimization led to novel purine based derivatives with favorable in vitro ADME profiles, which might represent promising starting points for future development of new inhaled drug candidates.

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.

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in past decades. Scaffold hopping is an effective approach for drug design. The kinase ATP-binding pocket is highly conserved, crossing the whole kinase family. This provides an opportunity to develop a scaffold hopping approach to explore diversified scaffolds among various kinase inhibitors. In this work, we report the SyntaLinker-Hybrid scheme for kinase inhibitor scaffold hopping. With this scheme, we replace molecular fragments bound at the conserved kinase hinge region with deep generative models. Thus, we are able to generate new kinase-inhibitor-like structures hybridizing the privileged fragments against the hinge region. We demonstrate that this scheme allows generation of kinase-inhibitor-like molecules with novel scaffolds, while retaining the binding features of existing kinase inhibitors. This work can be employed in lead identification against kinase targets.

Synthesis and Evaluation of Novel 1,2,6-Thiadiazinone Kinase Inhibitors as Potent Inhibitors of Solid Tumors

A focused series of substituted 4H-1,2,6-thiadiazin-4-ones was designed and synthesized to probe the anti-cancer properties of this scaffold. Insights from previous kinase inhibitor programs were used to carefully select several different substitution patterns. Compounds were tested on bladder, prostate, pancreatic, breast, chordoma, and lung cancer cell lines with an additional skin fibroblast cell line as a toxicity control. This resulted in the identification of several low single digit micro molar compounds with promising therapeutic windows, particularly for bladder and prostate cancer. A number of key structural features of the 4H-1,2,6-thiadiazin-4-one scaffold are discussed that show promising scope for future improvement.

Fragment Binding to Kinase Hinge: If Charge Distribution and Local pKa Shifts Mislead Popular Bioisosterism Concepts

Medicinal‐chemistry optimization follows strategies replacing functional groups and attaching larger substituents at a promising lead scaffold. Well‐established bioisosterism rules are considered, however, it is difficult to estimate whether the introduced modifications really match the required properties at a binding site. The electron density distribution and pK_a values are modulated influencing protonation states and bioavailability. Considering the adjacent H‐bond donor/acceptor pattern of the hinge binding motif in a kinase, we studied by crystallography a set of fragments to map the required interaction pattern. Unexpectedly, benzoic acid and benzamidine, decorated with the correct substituents, are totally bioisosteric just as carboxamide and phenolic OH. A mono‐dentate pyridine nitrogen out‐performs bi‐dentate functionalities. The importance of correctly designing pK_a values of attached functional groups by additional substituents at the parent scaffold is rendered prominent.

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.

In Silico Design and Analysis of a Kinase-Focused Combinatorial Library Considering Diversity and Quality

A structurally diverse, high-quality, and kinase-focused database plays a critical role in finding hits or leads in kinase drug discovery. Here, we propose a workflow for designing a virtual kinase-focused combinatorial library using existing structures. Based on the analysis of known protein kinase inhibitors (PKIs), detailed fragment optimization, fragment selection, fragment linking, and a molecular filtering scheme were defined. Quick recognition of core fragments that can possibly form dual hydrogen bonds with the hinge region of the ATP-pocket was proposed. Furthermore, three diversity and four quality metrics were chosen for compound library analysis, which can be applied to databases with over 30 million structures. Compared with 13 commercial libraries, our protocol demonstrates a special advantage in terms of good skeleton diversity, acceptable fingerprint diversity, balanced scaffold distribution, and high quality, which can work well not only on existing PKIs, but also on four chosen commercial libraries. Overall, the strategy can greatly facilitate the expansion of a desirable chemical space for kinase drug discovery.

Evolution of Small Molecule Kinase Drugs

The development of small molecule kinase drugs is a rapidly evolving field and represents one of the most important research areas within oncology. This innovation letter provides an overview and analysis of approved kinase drugs according to their WHO registration (INN) dates, primary biological targets, and selectivity and structural similarities, which are also depicted in an associated poster. It also discusses new trends in kinase drug discovery programs such as new kinase targets, novel mechanisms of action, and diverse indications.

3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery

eScience technologies are needed to process the information available in many heterogeneous types of protein-ligand interaction data and to capture these data into models that enable the design of efficacious and safe medicines. Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i) structure-based bioactivity data mapping, ii) structure-based identification of scaffold replacement strategies for ligand design, iii) ligand-based target prediction, iv) protein sequence-based binding site identification and ligand repurposing, and v) structure-based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well-established standards allows the re-use of these protocols and facilitates the design of customized computer-aided drug discovery workflows.