Kernel-Elastic Autoencoder for Molecular Design

We introduce the Kernel-Elastic Autoencoder (KAE), a self-supervised generative model based on the transformer architecture with enhanced performance for molecular design. KAE is formulated based on two novel loss functions: modified maximum mean discrepancy and weighted reconstruction. KAE addresses the long-standing challenge of achieving valid generation and accurate reconstruction at the same time. KAE achieves remarkable diversity in molecule generation while maintaining near-perfect reconstructions on the independent testing dataset, surpassing previous molecule-generating models. KAE enables conditional generation and allows for decoding based on beam search resulting in state-of-the-art performance in constrained optimizations. Furthermore, KAE can generate molecules conditional to favorable binding affinities in docking applications as confirmed by AutoDock Vina and Glide scores, outperforming all existing candidates from the training dataset. Beyond molecular design, we anticipate KAE could be applied to solve problems by generation in a wide range of applications

Promotion of cytoplasmic vacuolation-mediated cell death of human prostate cancer PC-3 cells by oxidative stress induced by daucusol, a new guaiane-type sesquiterpenoid from Daucus carota L.

We investigated the antitumor activity of daucusol (DS) derived from Daucus carota L. in PC-3, A549 and HeLa cell lines by the MTT assay. Optical microscopy revealed that exposure of PC-3 cells to DS resulted in cytoplasmic vacuolation. Flow cytometry analysis of the phase of the cell cycle did not reveal a sub-G1 peak, and no caspase-dependent activation was observed after DS treatment. The levels of endoplasmic reticulum (ER) stress biomarkers, LC3B-II and ubiquitinated proteins were increased. It was also observed that oxidative stress played an important role in the activation of the cytoplasmic vacuolation-mediated cell-death pathway. In vivo, DS inhibited tumor growth in nude mice by 39.13% compared to the vehicle. Protein expression in the tumor tissue was consistent with their expression in vitro. Our findings indicate that DS induced cytoplasmic vacuolation-mediated death in PC-3 cells by triggering oxidative stress and suggest that targeting this pathway could serve as a novel therapeutic approach for prostate cancer

Feed Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction

Density functional theory (DFT) has become a popular method to model transition state (TS) energies to predict enantioselectivity, but the associated errors present challenges. Machine learning has emerged as a powerful tool to model enantioselectivity but generally requires large datasets for training. Herein, we describe the development of a feed forward neural network for predicting enantioselectivity of the Negishi cross-coupling reaction with Boehringer Ingelheim (BI)-type phosphines. The selectivity predicted from DFT TS energies is upgraded through the neural network based on input features including geometries, electron population, and dispersive interactions. This new approach to modeling enantioselectivity is compared to conventional approaches, including exclusive use of DFT energies, and data science approaches using features from ligands or ground states with simple neural network architectures

Leverage of nuclease-deficient CasX for preventing pathological angiogenesis

Gene editing with a CRISPR/Cas system is a novel potential strategy for treating human diseases. Pharmacological inhibition of phosphoinositide 3-kinase (PI3K) δ suppresses retinal angiogenesis in a mouse model of oxygen-induced retinopathy. Here we show that an innovative system of adeno-associated virus (AAV)-mediated CRISPR/nuclease-deficient (d)CasX fused with the Krueppel-associated box (KRAB) domain is leveraged to block (81.2% ± 6.5%) in vitro expression of p110δ, the catalytic subunit of PI3Kδ, encoded by Pik3cd. This CRISPR/dCasX-KRAB (4, 269 bp) system is small enough to be fit into a single AAV vector. We then document that recombinant AAV serotype (rAAV)1 efficiently transduces vascular endothelial cells from pathologic retinal vessels, which show high expression of p110δ; furthermore, we demonstrate that blockade of retinal p110δ expression by intravitreally injected rAAV1-CRISPR/dCasX-KRAB targeting the Pik3cd promoter prevents (32.1% ± 5.3%) retinal p110δ expression as well as pathological retinal angiogenesis in a mouse model of oxygen-induced retinopathy. These data establish a strong foundation for treating pathological angiogenesis by AAV-mediated CRISPR interference with p110δ expression

Enhanced Ligand Discovery through Generative AI and Latent-Space Exploration: Application to the Mizoroki-Heck Reaction

The identification of catalysts that promote chemical reactions is a critical challenge in the production of pharmaceuticals. One of the main bottlenecks in this process is the synthesis of vast libraries of precatalysts, although assessing catalyst effectiveness can be rapidly conducted through high-throughput experimentation. The rational design and development of high-performing precatalysts can circumvent this challenge and lead to important advances. In this study, we apply the transformer-based Kernel-Elastic Autoencoder (KAE) equipped with a conditioned latent space, enabling the targeted generation of ligands with desired steric and electronic properties. Our KAE model has facilitated the identification of a monodentate alkynylphosphine, dubbed MachinePhos A, as an effective precatalyst for forming carbon-carbon bond. Its utility was demonstrated experimentally in the Mizoroki-Heck reaction, using a variety of nitrogen-rich arenes pertinent to pharmaceutical applications