Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties
Institute of Ion Physics and Applied Physics, University of Innsbruck（因斯布鲁克大学离子物理与应用物理研究所）；Tashkent Chemical Technological Institute（塔什干化工技术学院）；School of Molecular Science and Engineering, Vidyasirimedhi Institute of Science and Technology（维达西梅迪科学技术学院分子科学与工程学院）
Shokirbek Shermukhamedov, Dilorom Mamurjonova, Michael Probst
We introduce the elEmBERT model for chemical classification tasks. It is based on deep learning techniques, such as a multilayer encoder architecture. We demonstrate the opportunities offered by our approach on sets of organic, inorganic and crystalline compounds. In particular, we developed and tested the model using the Matbench and Moleculenet benchmarks, which include crystal properties and drug design-related benchmarks. We also conduct an analysis of vector representations of chemical compounds, shedding light on the underlying patterns in structural data. Our model exhibits exceptional predictive capabilities and proves universally applicable to molecular and material datasets. For instance, on the Tox21 dataset, we achieved an average precision of 96%, surpassing the previously best result by 10%.
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction
Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University（清华大学交叉信息研究院）；Zhejiang Lab（之江实验室）；The Chinese University of Hong Kong（香港中文大学）
Multimodal machine learning with large language embedding model for polymer property prediction
Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States（材料科学与工程系，德雷克塞尔大学）；Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States（化学系，宾夕法尼亚大学）
Rigorous ab initio quantum embedding for quantum chemistry using Green's function theory: screened interaction, non-local self-energy relaxation, orbital basis, and chemical accuracy
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
Kakao Corporation（Kakao公司）；LG Chem（LG化学）；Seoul National University（首尔国立大学）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；Seoul National University of Science and Technology（首尔国立科技大学）
QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
TUD Dresden University of Technology（德累斯顿工业大学）；Pfizer Worldwide R&D（辉瑞全球研发中心）；Center for Advanced Systems Understanding (CASUS)（高级系统理解中心（CASUS））；Helmholtz Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）
Farthest Point Sampling in Property Designated Chemical Feature Space as a General Strategy for Enhancing the Machine Learning Model Performance for Small Scale Chemical Dataset
Tianjin University（天津大学）；Collaborative Innovation Center of Chemical Science and Engineering (Tianjin)（（天津）化工协同创新中心）
