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From Latent Manifolds to Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework for Family-Based Kinase Ligand Design

Newlypublished by gennady verkhivker, et al.

🔍 Key findings: Novel generative framework integrates ChemVAE-based latent space modeling with chemically interpretable structural similarity metric (Kinase Likelihood Score) and Bayesian optimization for SRC kinase ligand design, demonstrating kinase scaffolds spanning 37 protein kinase families spontaneously organize into low-dimensional manifold with chemically distinct carboxyl groups revealing degeneracy in scaffold encoding — local sampling successfully converts scaffolds from other kinase families into novel SRC-like chemotypes accounting for ~40% of high-similarity cutoffs.

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Scaffold-aware artificial intelligence (AI) models enable systematic exploration of chemical space conditioned on protein-interacting ligands, yet the representational principles governing their behavior remain poorly understood. The computational representation of structurally complex kinase small molecules remains a formidable challenge due to the high conservation of ATP active site architecture across the kinome and the topological complexity of structural scaffolds in current generative AI frameworks. In this study, we present a diagnostic, modular and chemistry-first generative framework for design of targeted SRC kinase ligands by integrating ChemVAE-based latent space modeling, a chemically interpretable structural similarity metric (Kinase Likelihood Score), Bayesian optimization, and cluster-guided local neighborhood sampling.

How a broken DNA repair tool accelerates aging

Although DNA is tightly packed and protected within the cell nucleus, it is constantly threatened by damage from normal metabolic processes or external stressors such as radiation or chemical substances. To counteract this, cells rely on an elaborate network of repair mechanisms. When these systems fail, DNA damage can accumulate, impair cellular function, and contribute to cancer, aging, and degenerative diseases.

One particularly severe form of DNA damage are the so-called DNA–protein crosslinks (DPCs), in which proteins become attached to DNA. DPCs can arise from alcohol consumption, exposure to substances such as formaldehyde or other aldehydes, or from errors made by enzymes involved in DNA replication and repair. Because DPCs can cause serious errors during cell division by stalling DNA replication, DNA–protein crosslinks pose a serious threat to genome integrity.

The enzyme SPRTN removes DPCs by cleaving the DNA-protein crosslinks. SPRTN malfunctions, for example as a result of mutations, may predispose individuals to developing bone deformities and liver cancer in their teenage years. This rare genetic disorder is known as Ruijs-Aalfs syndrome. Its underlying mechanism remains poorly understood, and there are no specific therapies.

Scientists teach microorganisms to build molecules with light

Researchers are continually looking for new ways to hack the cellular machinery of microbes like yeast and bacteria to make products that are useful for humans and society. In a new proof-of-concept study, a team from the Carl R. Woese Institute for Genomic Biology showed they can expand the biosynthetic capabilities of these microbes by using light to help access new types of chemical transformations.

The paper, published in Nature Catalysis, demonstrates how the bacteria Escherichia coli can be engineered to produce these new molecules in vivo, using light-driven enzymatic reactions. This framework sets the foundation for future development in the emerging field of photobiocatalysis.

“Photobiocatalysis is basically light-activated catalysis by enzymes. Without light, the target enzyme cannot catalyze a reaction. When light is added, the target enzyme will be activated,” said Huimin Zhao (BSD leader/CAMBERS/CGD/MMG), Steven L. Miller Chair of Chemical and Biomolecular Engineering. “We have published many papers showing that it is possible to combine photocatalysis with enzyme catalysis to create a new class of photoenzymes. These artificial photoenzymes can catalyze selective reactions that cannot be achieved by natural enzymes and are also very difficult, or sometimes even not possible, with chemical catalysis.”

Shining a light on sustainable sulfur-rich polymers that stay recyclable

For the first time, scientists have used ultraviolet (UV) light, a low-cost and readily available energy source, to successfully synthesize more sustainable and recyclable polymer materials. Led by green chemistry experts at Flinders University, the development is a major step in making polymers high in sulfur content for more sustainable plastic alternatives using waste materials.

Their paper, “Making and Unmaking Poly(trisulfides) with Light: Precise Regulation of Radical Concentrations via Pulsed LED Irradiation” is published in the Journal of the American Chemical Society.

3D covalent organic framework offers sustainable solution for wastewater treatment

Industrial dye pollution remains one of the most persistent and hazardous challenges in global wastewater management. The dyes from textile and chemical manufacturing sectors are difficult to remove, non-biodegradable, and can be toxic to plants, animals, and humans. However, conventional treatment technologies for dyes often fail to efficiently purify the wastewater without significant trade-offs.

To remedy this issue, researchers from Tohoku University developed a three-dimensional covalent organic framework (COF), TU-123, that enables highly efficient and selective removal of anionic dyes from contaminated water.

The highly porous COF acts like a sponge—trapping dyes for easier separation. This work establishes a new structural blueprint for constructing highly connected imidazole-linked three-dimensional COFs. Furthermore, it opens sustainable pathways for advanced wastewater purification technologies.

Biomolecular condensates sustain pH gradients at equilibrium through charge neutralization

PH is a critical regulator of (bio)chemical processes and therefore tightly regulated in nature. Now, proteins have been shown to possess the functionality to drive pH gradients without requiring energy input or membrane enclosure but through condensation. Protein condensates can drive unique pH gradients that modulate biochemical activity in both living and artificial systems.

New study unveils ultra-high sensitivity broadband flexible photodetectors

A research team, affiliated with UNIST, has unveiled a flexible photodetector, capable of converting light across a broad spectrum—from visible to near-infrared—into electrical signals. This innovation promises significant advancements in technologies that require simultaneous detection of object colors and internal structures or materials.

Led by Professor Changduk Yang from the Department of Energy & Chemical Engineering, the research team developed perovskite-organic heterojunction photodetectors (POH-PDs) that combine high sensitivity with exceptional accuracy in the near-infrared (NIR) region. The findings have been published in Advanced Functional Materials.

Photodetectors are essential components in numerous applications, including smartphone displays that automatically adjust brightness and security systems that utilize vein recognition.

Measuring metabolic flux in brain cancer patients with AI based digital twin

The study, published in Cell Metabolism, builds on previous research showing that some gliomas can be slowed down through the patient’s diet. If a patient isn’t consuming certain protein building blocks, called amino acids, then some tumors are unable to grow. However, other tumors can produce these amino acids for themselves, and can continue growing anyway. Until now, there was no easy way to tell which patients would benefit from dietary restrictions.

The digital twin’s ability to map metabolic activity in tumors also helped determine whether a drug that prevents tumors from producing a building block for replicating and repairing DNA would work, as some cells can obtain that molecule from their environments.

To overcome challenges in mapping tumor metabolism inside the brain, the team developed a computer-based “digital twin” that can predict how an individual patient’s brain tumor will react to each treatment.

“Typically, metabolic measurements during surgeries to remove tumors can’t provide a clear picture of tumor metabolism—surgeons can’t observe how metabolism varies with time, and labs are limited to studying tissues after surgery. By integrating limited patient data into a model based on fundamental biology, chemistry and physics, we overcame these obstacles,” said a co-corresponding author of the study.

The digital twin uses patient data obtained through blood draws, metabolic measurements of the tumor tissue and the tumor’s genetic profile. The digital twin then calculates the speed at which the cancer cells consume and process nutrients, known as metabolic flux.

“This is the first time a machine learning and AI-based approach has been used to measure metabolic flux directly in patient tumors,” said a co-first author of the study.

The researchers built a type of deep learning model called a convolutional neural network and trained it on synthetic patient data, generated based on known biology and chemistry and constrained by measurements from eight patients with glioma who were infused with labeled glucose during surgery. By comparing their computer models with different data from six of those patients, they found the digital twins could predict metabolic activity with high accuracy. In experiments conducted on mice, the team confirmed that the diet only slowed tumor growth in mice that the digital twin had identified as good candidates for the treatment.

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Forever Chemicals Linked to Multiple Sclerosis in Concerning New Study

People who are exposed to certain forever chemicals may be at greater risk of developing multiple sclerosis (MS), according to new research.

No one knows why that is, but it could help explain why, over the past 30 years, the prevalence of MS has increased by an average of 26 percent globally. In some nations, cases have more than doubled since 1990.

MS is an autoimmune disease of the central nervous system with no known singular cause and no known cure.

MXene nanoscrolls could improve energy storage, biosensors and more

Researchers from Drexel University who discovered a versatile type of two-dimensional conductive nanomaterial called MXene nearly a decade and a half ago, have now reported on a process for producing its one-dimensional cousin: the MXene nanoscroll. The group posits that these materials, which are 100 times thinner than human hair yet more conductive than their two-dimensional counterparts, could be used to improve the performance of energy storage devices, biosensors and wearable technology.

Their finding, published in the journal Advanced Materials, offers a scalable method for producing the nanoscrolls from a MXene precursor with precise control over their shape and chemical structures.

“Two-dimensional morphology is very important in many applications. However, there are applications where 1D morphology is superior,” said Yury Gogotsi, Ph.D., Distinguished University and Bach professor in Drexel’s College of Engineering, who was a corresponding author of the paper.

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