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. Given a molecular graph and random noise, the generator produces a conformation in two stages. First, it constructs a conformation in a rotation and translation invariant representation─internal coordinates. In the second step, the model predicts the distances between neighboring atoms and performs a few fast optimization steps to refine the initial conformation. The proposed model considers conformation energy, achieving comparable space coverage, and diversity metrics results.

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 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. C1 exhibited significant antiproliferative properties in DNA repair-compromised cells and demonstrated favorable pharmacokinetics, showcasing that 3-hydroxymethyl-azetidine is an effective bio-isostere of pyrrolidin-3-ol and emphasizing the potential of AI in medicinal chemistry for precise molecular modifications.

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 âˆ¼ 13 % of all tumors. Thus, MAT2A holds great promise as a novel anticancer target. Here, we report a novel series of MAT2A inhibitors generated by a fragment growing approach from AZ-28, a low-molecular weight MAT2A inhibitor with promising pre-clinical properties. X-ray co-crystal structure revealed that compound 7 fully occupies the allosteric pocket of MAT2A as a single molecule mimicking MAT2B. By introducing additional backbone interactions and rigidifying the requisite linker extensions, we generated compound 8, which exhibited single digit nanomolar enzymatic and sub-micromolar cellular inhibitory potency for MAT2A.

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.

Chemistry42: An AI-Driven Platform for Molecular Design and Optimization.

Chemistry42 is a software platform for de novo small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methodologies. Chemistry42 efficiently generates novel molecular structures with optimized properties validated in both in vitro and in vivo studies and is available through licensing or collaboration. Chemistry42 is the core component of Insilico Medicine's Pharma.ai drug discovery suite. Pharma.ai also includes PandaOmics for target discovery and multiomics data analysis, and inClinico─a data-driven multimodal forecast of a clinical trial's probability of success (PoS). In this paper, we demonstrate how the platform can be used to efficiently find novel molecular structures against DDR1 and CDK20.

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry.

De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly developing fields with lead candidate discovery in the drug development process. First, artificial intelligence has already been demonstrated to successfully accelerate conventional drug design approaches. Second, quantum computing has demonstrated promising potential in different applications, such as quantum chemistry, combinatorial optimizations, and machine learning. This article explores hybrid quantum-classical generative adversarial networks (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of a GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules. We also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of both generated molecule properties and the achieved KL divergence. However, the hybrid quantum-classical GANs still face challenges in generating unique and valid molecules compared to their classical counterparts.

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

Salt-inducible kinase 2 (SIK2) has been recognized as a potential target for anti-inflammation and anti-cancer therapy. In this paper, based on the binding pose of the reported compound (GLPG-3970, 3) with AlphaFold protein structure, a series of hinge cores were generated via AI-generative models (Chemistry42). After the molecular docking, synthesis, and biological evaluation, a hit molecule (7f) targeting SIK2 was obtained with a novel scaffold. Further SAR exploration led to the discovery of compound 8g with superior potency against SIK2 compared with the reported inhibitors. Furthermore, 8g also demonstrated excellent selectivity over other AMPK kinases, favorable in vitro ADMET profiles and decent cellular activities. This work provides an alternative approach to the discovery of novel and selective kinase inhibitors.

COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity.

Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks.

Adversarial Threshold Neural Computer for Molecular de Novo Design.

In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp3-rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection were used as a training data set. For the different functions, ATNC outperforms ORGANIC. Combined with the IDC, ATNC generates 72% of valid and 77% of unique SMILES strings, while ORGANIC generates only 7% of valid and 86% of unique SMILES strings. For each set of molecules generated by ATNC and ORGANIC, we analyzed distributions of four molecular descriptors (number of atoms, molecular weight, logP, and tpsa) and calculated five chemical statistical features (internal diversity, number of unique heterocycles, number of clusters, number of singletons, and number of compounds that have not been passed through medicinal chemistry filters). Analysis of key molecular descriptors and chemical statistical features demonstrated that the molecules generated by ATNC elicited better druglikeness properties. We also performed in vitro validation of the molecules generated by ATNC; results indicated that ATNC is an effective method for producing hit compounds.

Aging Chart: a community resource for rapid exploratory pathway analysis of age-related processes.

Aging research is a multi-disciplinary field encompassing knowledge from many areas of basic, applied and clinical research. Age-related processes occur on molecular, cellular, tissue, organ, system, organismal and even psychological levels, trigger the onset of multiple debilitating diseases and lead to a loss of function, and there is a need for a unified knowledge repository designed to track, analyze and visualize the cause and effect relationships and interactions between the many elements and processes on all levels. Aging Chart (http://agingchart.org/) is a new, community-curated collection of aging pathways and knowledge that provides a platform for rapid exploratory analysis. Building on an initial content base constructed by a team of experts from peer-reviewed literature, users can integrate new data into biological pathway diagrams for a visible, intuitive, top-down framework of aging processes that fosters knowledge-building and collaboration. As the body of knowledge in aging research is rapidly increasing, an open visual encyclopedia of aging processes will be useful to both the new entrants and experts in the field.

druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico.

Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models.

Discovery of Novel and Potent Prolyl Hydroxylase Domain-Containing Protein (PHD) Inhibitors for The Treatment of Anemia.

Stabilization of hypoxia-inducible factor (HIF) by inhibiting prolyl hydroxylase domain enzymes (PHDs) represents a breakthrough in treating anemia associated with chronic kidney disease. Here, we identified a novel scaffold for noncarboxylic PHD inhibitors by utilizing structure-based drug design (SBDD) and generative models. Iterative optimization of potency and solubility resulted in compound 15 which potently inhibits PHD thus stabilizing HIF-α in vitro. X-ray cocrystal structure confirmed the binding model was distinct from previously reported carboxylic acid PHD inhibitors by pushing away the R383 and Y303 residues resulting in a larger inner subpocket. Furthermore, compound 15 demonstrated a favorable in vitro/in vivo absorption, distribution, metabolism, and excretion (ADME) profile, low drug-drug interaction risk, and clean early safety profiling. Functionally, oral administration of compound 15 at 10 mg/kg every day (QD) mitigated anemia in a 5/6 nephrectomy rat disease model.

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 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.

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. We employed instruction tuning, where specific task-related instructions are utilized to fine-tune nach0 for the final set of tasks. To train nach0 effectively, we leverage the NeMo framework, enabling efficient parallel optimization of both base and large model versions. Extensive experiments demonstrate that our model outperforms state-of-the-art baselines on single-domain and cross-domain tasks. Furthermore, it can generate high-quality outputs in molecular and textual formats, showcasing its effectiveness in multi-domain setups.

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. We emphasize the implications of TNIK signaling in metabolic signaling and evaluate the translational potential of these discoveries. We also highlight how TNIK's role in many biological processes converges upon several hallmarks of aging. We conclude by discussing the therapeutic landscape of TNIK-targeting drugs and the recent success of clinical trials targeting TNIK.

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

Introduction: 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. Methods: We developed Quantum-assisted Fragment-based Automated Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. QFASG was applied to generating new structures of CAMKK2 and ATM inhibitors. Results: New low-micromolar inhibitors of CAMKK2 and ATM were designed using the algorithm. Discussion: These findings highlight the algorithm's potential in designing primary hits for further optimization and showcase the capabilities of QFASG as an effective tool in this field.

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. Here we report the design and development of compound 4, a novel small molecule PTPN2/N1 inhibitor demonstrating nanomolar inhibitory potency, good in vivo oral bioavailability, and robust in vivo antitumor efficacy.

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. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.