Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors.

We introduce a quantum-classical generative model for small-molecule design, specifically targeting KRAS inhibitors for cancer therapy. We apply the method to design, select and synthesize 15 proposed molecules that could notably engage with KRAS for cancer therapy, with two holding promise for future development as inhibitors. This work showcases the potential of quantum computing to generate experimentally validated hits that compare favorably against classical models.

Discovery of Potent, Highly Selective, and Orally Bioavailable MTA Cooperative PRMT5 Inhibitors with Robust Antitumor Activity.

Protein arginine methyltransferase 5 (PRMT5), which catalyzes the symmetric dimethylation of arginine residues on target proteins, plays a critical role in gene expression regulation, RNA processing, and signal transduction. Aberrant PRMT5 activity has been implicated in cancers and other diseases, making it a potential therapeutic target. Here, we report the discovery of a methylthioadenosine (MTA) cooperative PRMT5 inhibitor.

AttenhERG: a reliable and interpretable graph neural network framework for predicting hERG channel blockers.

Cardiotoxicity, particularly drug-induced arrhythmias, poses a significant challenge in drug development, highlighting the importance of early-stage prediction of human ether-a-go-go-related gene (hERG) toxicity. hERG encodes the pore-forming subunit of the cardiac potassium channel. Traditional methods are both costly and time-intensive, necessitating the development of computational approaches.

Intestinal mucosal barrier repair and immune regulation with an AI-developed gut-restricted PHD inhibitor.

Hypoxia-inducible factor prolyl hydroxylase (PHD) inhibitors have been approved for treating renal anemia yet have failed clinical testing for inflammatory bowel disease because of a lack of efficacy. Here we used a multimodel multimodal generative artificial intelligence platform to design an orally gut-restricted selective PHD1 and PHD2 inhibitor that exhibits favorable safety and pharmacokinetic profiles in preclinical studies. ISM012-042 restores intestinal barrier function and alleviates gut inflammation in multiple experimental colitis models.

Discovery of Pyridine-2-Carboxamides Derivatives as Potent and Selective HPK1 Inhibitors for the Treatment of Cancer.

Hematopoietic progenitor kinase 1 (HPK1) has emerged as an attractive target for immunotherapy due to its critical role in T cell activation and proliferation. The major challenge in developing HPK1 inhibitors lies in balancing kinase selectivity, pharmacokinetic (PK) properties, and therapeutic efficacy. In this study, we report a series of pyridine-2-carboxamide analogues demonstrating strong HPK1 inhibitory activity in enzymatic and cellular assays, along with good kinase selectivity.

Design, Synthesis, and Biological Evaluation of a Series of Spiro Analogues as Novel HPK1 Inhibitors.

Hematopoietic progenitor kinase 1 (HPK1) negatively affects T cell activation and proliferation and is a promising target for immunotherapy. Although HPK1 inhibitors have shown promising efficacy in preclinical models, none have been approved for clinical use. One significant challenge in developing an HPK1 inhibitor is the difficulty in designing a potent inhibitor with good kinase selectivity and pharmacokinetic properties.

Discovery of a Novel Macrocyclic Noncovalent CDK7 Inhibitor for Cancer Therapy.

Cyclin-dependent kinase 7 (CDK7) is a key regulator of the cell cycle and transcription, making it a promising target for cancer therapy. Although current CDK7 inhibitors have improved in their selectivity and druglike properties, CDK7 inhibitors have failed to progress through clinical development due to severe gastrointestinal and hematotoxic side effects. To mitigate these limitations, we have developed novel, macrocyclic, noncovalent CDK7 hit compounds and using a macrocyclization platform that has optimized these compounds from SY-5609, a leading clinical asset.

Discovery of pyrrolopyrimidinone derivatives as potent PKMYT1 inhibitors for the treatment of cancer.

The protein kinase PKMYT1 is responsible for inhibitory CDK1 phosphorylation, thus playing a central role in regulating the G2/M cell cycle checkpoint. As many cancers have dysfunctional cell cycle checkpoint signaling, PKMYT1 inhibition is emerging as an attractive target in advanced tumors. PKMYT1 inhibitors, however, have encountered difficulties in balancing biological efficacy, on-target specificity, and favorable stability and other drug-like properties.

Discovery of Bis-imidazolecarboxamide Derivatives as Novel, Potent, and Selective TNIK Inhibitors for the Treatment of Idiopathic Pulmonary Fibrosis.

Traf2- and Nck-interacting kinase (TNIK) has been identified as a promising therapeutic target for the treatment of fibrosis-driven diseases. Utilizing a structure-based drug design workflow, we developed a series of potent TNIK inhibitors that modulate the conformation of the gatekeeper Met105 side chain and access the TNIK back pocket. The lead optimization efforts culminated in the discovery of the recently reported compound (INS018_055), a novel TNIK inhibitor.

Discovery of 1(2H)-phthalazinone and 1(2H)-isoquinolinone derivatives as potent hematopoietic progenitor kinase 1 (HPK1) inhibitors.

Although immune checkpoint inhibitors (ICIs) have been a revelation for treating several cancers, an unmet need remains to broaden ICI therapeutic scope and increase their response rates in clinical trials. Hematopoietic progenitor kinase 1 (HPK1) is a negative regulator of T cell activation and has previously been identified as a promising target for immunotherapy. Herein, we report the discovery of a series of HPK1 inhibitors with novel 1(2H)-phthalazinone and 1(2H)-isoquinolinone scaffolds.

Precious2GPT: the combination of multiomics pretrained transformer and conditional diffusion for artificial multi-omics multi-species multi-tissue sample generation.

Synthetic data generation in omics mimics real-world biological data, providing alternatives for training and evaluation of genomic analysis tools, controlling differential expression, and exploring data architecture. We previously developed Precious1GPT, a multimodal transformer trained on transcriptomic and methylation data, along with metadata, for predicting biological age and identifying dual-purpose therapeutic targets potentially implicated in aging and age-associated diseases. In this study, we introduce Precious2GPT, a multimodal architecture that integrates Conditional Diffusion (CDiffusion) and decoder-only Multi-omics Pretrained Transformer (MoPT) models trained on gene expression and DNA methylation data.

nach0: multimodal natural and chemical languages foundation model.

Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions. Our paper introduces a new foundation model, nach0, capable of solving various chemical and biological tasks: biomedical question answering, named entity recognition, molecular generation, molecular synthesis, attributes prediction, and others. nach0 is a multi-domain and multi-task encoder-decoder LLM pre-trained on unlabeled text from scientific literature, patents, and molecule strings to incorporate a range of chemical and linguistic knowledge.

TNIK's emerging role in cancer, metabolism, and age-related diseases.

Traf2- and Nck-interacting kinase (TNIK) has emerged as a key regulator of pathological metabolic signaling in several diseases and is a promising drug target. Originally studied for its role in cell migration and proliferation, TNIK possesses several newly identified functions that drive the pathogenesis of multiple diseases. Specifically, we evaluate TNIK's newfound roles in cancer, metabolic disorders, and neuronal function.

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 , a novel small molecule CDK8/19 inhibitor.

COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework.

The fast and accurate conformation space modeling is an essential part of computational approaches for solving ligand and structure-based drug discovery problems. Recent state-of-the-art diffusion models for molecular conformation generation show promising distribution coverage and physical plausibility metrics but suffer from a slow sampling procedure. We propose a novel adversarial generative framework, COSMIC, that shows comparable generative performance but provides a time-efficient sampling and training procedure.

Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an study.

The significance of automated drug design using virtual generative models has steadily grown in recent years. While deep learning-driven solutions have received growing attention, only a few modern AI-assisted generative chemistry platforms have demonstrated the ability to produce valuable structures. At the same time, virtual fragment-based drug design, which was previously less popular due to the high computational costs, has become more attractive with the development of new chemoinformatic techniques and powerful computing technologies.

Synthesis and structure-activity optimization of azepane-containing derivatives as PTPN2/PTPN1 inhibitors.

Protein tyrosine phosphatases PTPN2 and PTPN1 (also known as PTP1B) have been implicated in a number of intracellular signaling pathways of immune cells. The inhibition of PTPN2 and PTPN1 has emerged as an attractive approach to sensitize T cell anti-tumor immunity. Two small molecule inhibitors have been entered the clinic.

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.

Discovery of 3-hydroxymethyl-azetidine derivatives as potent polymerase theta inhibitors.

Inhibition of the low fidelity DNA polymerase Theta (Polθ) is emerging as an attractive, synthetic-lethal antitumor strategy in BRCA-deficient tumors. Here we report the AI-enabled development of 3-hydroxymethyl-azetidine derivatives as a novel class of Polθ inhibitors featuring central scaffolding rings. Structure-based drug design first identified A7 as a lead compound, which was further optimized to the more potent derivative B3 and the metabolically stable deuterated compound C1.

A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.

Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach.

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 and studies.

Discovery of novel MAT2A inhibitors by an allosteric site-compatible fragment growing approach.

The methionine adenosyltransferase MAT2A catalyzes the synthesis ofthe methyl donor S-adenosylmethionine (SAM) and thereby regulates critical aspects of metabolism and transcription. Aberrant MAT2A function can lead to metabolic and transcriptional reprogramming of cancer cells, and MAT2A has been shown to promote survival of MTAP-deficient tumors, a genetic alteration that occurs in ∼ 13 % of all tumors. Thus, MAT2A holds great promise as a novel anticancer target.

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.