Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning

Graph neural network (GNN)-based deep learning (DL) models have been widely implemented to predict the experimental aqueous solvation free energy, while its prediction accuracy has reached a plateau partly due to the scarcity of available experimental data. In order to tackle this challenge, we first build a large and diverse calculated data set Frag20-Aqsol-100K of aqueous solvation free energy with reasonable computational cost and accuracy via electronic structure calculations with continuum solvent models. Then, we develop a novel 3D atomic feature-based GNN model with the principal neighborhood aggregation (PNAConv) and demonstrate that 3D atomic features obtained from molecular mechanics-optimized geometries can significantly improve the learning power of GNN models in predicting calculated solvation free energies. Finally, we employ a transfer learning strategy by pre-training our DL model on Frag20-Aqsol-100K and fine-tuning it on the small experimental data set, and the fine-tuned model A3D-PNAConv-FT achieves the state-of-the-art prediction on the FreeSolv data set with a root-mean-squared error of 0.719 kcal/mol and a mean-absolute error of 0.417 kcal/mol using random data splits. These results indicate that integrating molecular modeling and DL would be a promising strategy to develop robust prediction models in molecular science. The source code and data are accessible at: https://yzhang.hpc.nyu.edu/IMA

Multitask Deep Ensemble Prediction of Molecular Energetics in Solution: From Quantum Mechanics to Experimental Properties

The past few years have witnessed significant advances in developing machine learning methods for molecular energetics predictions, including calculated electronic energies with high-level quantum mechanical methods and experimental properties, such as solvation free energy and logP. Typically, task-specific machine learning models are developed for distinct prediction tasks. In this work, we present a multitask deep ensemble model, sPhysNet-MT-ens5, which can simultaneously and accurately predict electronic energies of molecules in gas, water, and octanol phases, as well as transfer free energies at both calculated and experimental levels. On the calculated data set Frag20-solv-678k, which is developed in this work and contains 678,916 molecular conformations, up to 20 heavy atoms, and their properties calculated at B3LYP/6-31G* level of theory with continuum solvent models, sPhysNet-MT-ens5 predicts density functional theory (DFT)-level electronic energies directly from force field-optimized geometry within chemical accuracy. On the experimental data sets, sPhysNet-MT-ens5 achieves state-of-the-art performances, which predict both experimental hydration free energy with a RMSE of 0.620 kcal/mol on the FreeSolv data set and experimental logP with a RMSE of 0.393 on the PHYSPROP data set. Furthermore, sPhysNet-MT-ens5 also provides a reasonable estimation of model uncertainty which shows correlations with prediction error. Finally, by analyzing the atomic contributions of its predictions, we find that the developed deep learning model is aware of the chemical environment of each atom by assigning reasonable atomic contributions consistent with our chemical knowledge

Additional file 2 of Ectopic intrapulmonary thyroid masquerading as metastatic carcinoma of the lung: a rare case scenario

Supplementary Material

Research on Molecular Mechanism of Fructus Ligustri Lucidi against Osteoporosis based on Network Pharmacology

Abstract TCMSP platform of systematic pharmacology of traditional Chinese medicine This study aimed to investigate the molecular mechanism of Fructus Ligustri Lucidi (NZZ, Chinese abbreviation) against osteoporosis (OP) by means of network pharmacology.ChemDraw Professional 15.1 software and Molinspiration Smiles database were used to draw the chemical formulas of the components. The active ingredients and related target proteins of NZZ were searched in platform of systematic pharmacology of traditional Chinese medicine database, Drugbank, Therapeutic Target Database, SymMap and other databases. Gene Ontology(GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out on the selected target through Enrichr and KEGG Automatic Annotation databases, and their mechanism was studied. A total of 29 compounds and 140 corresponding targets, including 14 key targets and 14 protein factors in protein-protein interaction core network were obtained. The key targets were tumor necrosis factor(TNF), interleukin(IL)-6R and sestrogen receptor alpha. The number of GO items was 466 (P</div

Ternary ZnCo₂O₄ Nanowire Electrode Materials for High-Capacitance and Flexible Electrochemical Capacitors

Spinel-structured oxides are promising candidates for supercapacitor electrodes owing to their features of low price and environmental friendliness. However, their large-scale applications are restricted in view of their low energy density and electrical conductivity. In this work, we synthesize wire-like ZnCo2O4 nanomaterials by a facile hydrothermal avenue and a subsequent calcination process. The prepared samples possess a large specific surface area, which is beneficial for increasing active sites and shortening the ion diffusion channels. The as-assembled asymmetric supercapacitor delivers an energy density of 64 Wh kg–1 at 2880 W kg–1. And the capacitance can be maintained at 85% after 10,000 cycles at the current density of 2 A g–1. The device indicates excellent mechanical stability when bending various angles, revealing its potential application in portable energy storage devices

Additional file 1 of Ectopic intrapulmonary thyroid masquerading as metastatic carcinoma of the lung: a rare case scenario

Supplementary Material

Thermally Activated Delayed Fluorescence Sensitized Phosphorescence: A Strategy To Break the Trade-Off between Efficiency and Efficiency Roll-Off

Materials with thermally activated delayed fluorescence (TADF) realized 100% internal quantum efficiency (IQE) but suffered significant efficiency roll-off. Here, an exciton dynamics study reveals that materials with TADF may play opposite roles in affecting the efficiency roll-off: decreasing the triplet density due to the fast reverse intersystem crossing, on the one hand, and increasing the triplet density due to the weakened singlet radiation. We show theoretically and experimentally that TADF-sensitized phosphorescence can break this trade-off by exploiting the efficient Förster energy transfer and simultaneously achieve 100% IQE and low efficiency roll-off (with a critical current density of 460 mA cm^–2)

Image_1_Case Report: Tumor-to-tumor metastasis with prostate cancer metastatic to lung cancer: the first reported case.jpeg

Tumor-to-tumor metastasis (TTM) occurs rarely in tumor progression, but this event has significant clinical implications. Although the impact of TTM on patient prognosis and survival has been increasingly recognized, understanding of TTM biology and treatment is limited. Prostate cancer is among the most common malignancies threatening male health. Prostate cancer can potentially metastasize to primary lung Cancer; however, this is an exceedingly rare event. We here report for the first time a case of TTM from a prostate cancer to a coexisting primary lung cancer.</p

Image_2_Case Report: Tumor-to-tumor metastasis with prostate cancer metastatic to lung cancer: the first reported case.tif

Tumor-to-tumor metastasis (TTM) occurs rarely in tumor progression, but this event has significant clinical implications. Although the impact of TTM on patient prognosis and survival has been increasingly recognized, understanding of TTM biology and treatment is limited. Prostate cancer is among the most common malignancies threatening male health. Prostate cancer can potentially metastasize to primary lung Cancer; however, this is an exceedingly rare event. We here report for the first time a case of TTM from a prostate cancer to a coexisting primary lung cancer.</p

sj-pdf-1-pid-10.1177_09544070231205063 – Supplemental material for Study on dynamic prediction method for degradation state of electric drive system based on deep learning and uncertainty quantification

Supplemental material, sj-pdf-1-pid-10.1177_09544070231205063 for Study on dynamic prediction method for degradation state of electric drive system based on deep learning and uncertainty quantification by Zhen Wang, Lihui Zhao, Dongdong Zhang and Chuliang Yan in Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering</p