Discovery of novel and selective SIK2 inhibitors by the application of AlphaFold structures and generative models

Discovery of Novel SIK2/3 Inhibitors for the Potential Treatment of MEF2C+ Acute Myeloid Leukemia (AML)

The dual inhibition of SIK2/3 has been considered as a potential treatment approach for MEF2C-high acute myeloid leukemia (AML). Although diverse scaffolds of pan-SIK or SIK2/3 inhibitors have been reported, few of them showed sufficient in vitro or in vivo antitumor activity. Based on the proposed binding mode of the hit molecule (7), chemical space in the solvent/P-loop region was explored via fragment growing/replacement, supported by the generative chemistry platform. Further SAR exploration and ADME optimization led to the discovery of 7s, which exhibited excellent potency and strong selectivity in MEF2C high-expression cell lines over MEF2C-low cell lines. Moreover, oral administration of 7s was found to demonstrate significant tumor growth inhibition in a MV4-11 AML mice CDX model without any body weight loss. This work highlights the potential of targeting MEF2C-dependent AML by selective oral SIK2/3 inhibitors, which was supported by the generative models.

Accelerating discovery of bioactive ligands with pharmacophore-informed generative models

Deep generative models have advanced drug discovery but often generate compounds with limited structural novelty, providing constrained inspiration for medicinal chemists. To address this, we develop TransPharmer, a generative model that integrates ligand-based interpretable pharmacophore fingerprints with a generative pre-training transformer (GPT)-based framework for de novo molecule generation. TransPharmer excels in unconditioned distribution learning, de novo generation, and scaffold elaboration under pharmacophoric constraints. Its unique exploration mode could enhance scaffold hopping, producing structurally distinct but pharmaceutically related compounds. Its efficacy is validated through two case studies involving the dopamine receptor D2 (DRD2) and polo-like kinase 1 (PLK1). Notably, three out of four synthesized PLK1-targeting compounds show submicromolar activities, with the most potent, IIP0943, exhibiting a potency of 5.1 nM. Featuring a new 4-(benzo[b]thiophen-7-yloxy)pyrimidine scaffold, IIP0943 also has high PLK1 selectivity and submicromolar inhibitory activity in HCT116 cell proliferation. TransPharmer offers a promising tool for discovering structurally novel and bioactive ligands.

Utilization of an optimized AlphaFold protein model for structure-based design of a selective HDAC11 inhibitor with anti-neuroblastoma activity

AlphaFold is an artificial intelligence approach for predicting the three-dimensional (3D) structures of proteins with atomic accuracy. One challenge that limits the use of AlphaFold models for drug discovery is the correct prediction of folding in the absence of ligands and cofactors, which compromises their direct use. We have previously described the optimization and use of the histone deacetylase 11 (HDAC11) AlphaFold model for the docking of selective inhibitors such as FT895 and SIS17. Based on the predicted binding mode of FT895 in the optimized HDAC11 AlphaFold model, a new scaffold for HDAC11 inhibitors was designed, and the resulting compounds were tested in vitro against various HDAC isoforms. Compound 5a proved to be the most active compound with an IC50 of 365 nM and was able to selectively inhibit HDAC11. Furthermore, docking of 5a showed a binding mode comparable to FT895 but could not adopt any reasonable poses in other HDAC isoforms. We further supported the docking results with molecular dynamics simulations that confirmed the predicted binding mode. 5a also showed promising activity with an EC50 of 3.6 µM on neuroblastoma cells.

Understanding the roles of salt-inducible kinases in cardiometabolic disease

Salt-inducible kinases (SIKs) are serine/threonine kinases of the adenosine monophosphate-activated protein kinase family. Acting as mediators of a broad array of neuronal and hormonal signaling pathways, SIKs play diverse roles in many physiological and pathological processes. Phosphorylation by the upstream kinase liver kinase B1 is required for SIK activation, while phosphorylation by protein kinase A induces the binding of 14-3-3 protein and leads to SIK inhibition. SIKs are subjected to auto-phosphorylation regulation and their activity can also be modulated by Ca2+/calmodulin-dependent protein kinase in response to cellular calcium influx. SIKs regulate the physiological processes through direct phosphorylation on various substrates, which include class IIa histone deacetylases, cAMP-regulated transcriptional coactivators, phosphatase methylesterase-1, among others. Accumulative body of studies have demonstrated that SIKs are important regulators of the cardiovascular system, including early works establishing their roles in sodium sensing and vascular homeostasis and recent progress in pulmonary arterial hypertension and pathological cardiac remodeling. SIKs also regulate inflammation, fibrosis, and metabolic homeostasis, which are essential pathological underpinnings of cardiovascular disease. The development of small molecule SIK inhibitors provides the translational opportunity to explore their potential as therapeutic targets for treating cardiometabolic disease in the future.

PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinico─a data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases.

Discovery of a Novel and Potent Cyclin-Dependent Kinase 8/19 (CDK8/19) Inhibitor for the Treatment of Cancer

The mediator kinases CDK8 and CDK19 control the dynamic transcription of selected genes in response to various signals and have been shown to be hijacked to sustain hyperproliferation by various solid and liquid tumors. CDK8/19 is emerging as a promising anticancer therapeutic target. Here, we report the discovery of compound 12, a novel small molecule CDK8/19 inhibitor. This molecule demonstrated not only decent enzymatic and cellular activities but also remarkable selectivity in CDK and kinome panels. Besides, compound 12 also displayed favorable ADME profiles including low CYP1A2 inhibition, acceptable clearance, and high oral bioavailability in multiple preclinical species. Robust in vivo PD and efficacy studies in mice models further demonstrated its potential use as mono- and combination therapy for the treatment of cancers.

Discovery of macrocyclic CDK2/4/6 inhibitors with improved potency and DMPK properties through a highly efficient macrocyclic drug design platform

Cyclin-dependent kinases (CDKs) are critical cell cycle regulators that are often overexpressed in tumors, making them promising targets for anti-cancer therapies. Despite substantial advancements in optimizing the selectivity and drug-like properties of CDK inhibitors, safety of multi-target inhibitors remains a significant challenge. Macrocyclization is a promising drug discovery strategy to improve the pharmacological properties of existing compounds. Here we report the development of a macrocyclization platform that enabled the highly efficient discovery of a novel, macrocyclic CDK2/4/6 inhibitor from an acyclic precursor (NUV422). Using dihedral angle scan and structure-based, computer-aided drug design to select an optimal ring-closing site and linker length for the macrocycle, we identified compound 8 as a potent new CDK2/4/6 inhibitor with optimized cellular potency and safety profile compared to NUV422. Our platform leverages both experimentally-solved as well as generative chemistry-derived macrocyclic structures and can be deployed to streamline the design of macrocyclic new drugs from acyclic starting compounds, yielding macrocyclic compounds with enhanced potency and improved drug-like properties.

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.

Discovery of Tetrahydropyrazolopyrazine Derivatives as Potent and Selective MYT1 Inhibitors for the Treatment of Cancer

Breast and gynecological cancers are among the leading causes of death in women worldwide, illustrating the urgent need for innovative treatment options. We identified MYT1 as a promising new therapeutic target for breast and gynecological cancer using PandaOmics, an AI-driven target discovery platform. The synthetic lethal relationship of MYT1 in tumor cell lines with CCNE1 amplification enhanced this rationale. Through structure-based drug design, we developed a series of novel, potent, and highly selective inhibitors specifically targeting MYT1. Importantly, our lead compound, featuring a tetrahydropyrazolopyrazine ring, exhibits remarkable selectivity over WEE1, a related kinase associated with bone marrow suppression upon inhibition. Optimization of potency and physical properties resulted in the discovery of compound 21, a novel MYT1 inhibitor, exhibiting optimal pharmacokinetic properties and promising in vivo antitumor efficacy.