\u203b\u8b1b\u6f14\u30bf\u30a4\u30c8\u30eb\u3092\u30af\u30ea\u30c3\u30af\u3059\u308b\u3068\u30b7\u30e7\u30fc\u30c8\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/p>\n
Prof. J\u00fcrgen Bajorath\uff08University of Bonn\uff09 Prof. Alexander Tropsha\uff08The University of North Carolina at Chapel Hill\uff09 Prof. Yoshihiro Yamanishi \uff08Nagoya University\uff09 Prof. Yasushi Okuno\uff08Kyoto University\uff09 Dr. Sharon Bryant\uff08Inte:Ligand\uff09 Prof. Gisbert Schneider\uff08ETH Zurich\uff09 Assoc. Prof. Tomoyuki Miyao\uff08NAIST\uff09 Prof. Thierry Langer\uff08University of Vienna\uff09 Prof. Alexandre Varnek\uff08University of Strasbourg\uff09 Prof. Kenji Hori\uff08TS Technology\uff09 Prof. Jose L Medina-Franco\uff08National Autonomous University of Mexico\uff09 Prof. Dragos Horvath\uff08University of Strasbourg\uff09 Prof. Shigetaka Tomiya\uff08NAIST\uff09 Prof. Hanoch Senderowitz\uff08Bar-Ilan University\uff09 Prof. Mikiya Fujii\uff08NAIST\uff09 Prof. Manabu Sugimoto\uff08Daiichi University of Pharmacy\uff09 O-1\u00a0 \u00a0\u7530\u4e2d\u82f1\u4e5f (NAIST) O-2\u00a0 \u00a0\u5ca9\ufa11\u3000\u60a0\u4eba (NAIST) O-3\u00a0 \u00a0\u677e\u6e05\u512a\u6a39 (\u540d\u53e4\u5c4b\u5927) O-4\u00a0 \u00a0Kowit Hengphasatporn (\u7b51\u6ce2\u5927\u5b66) O-5\u00a0 \u00a0Pavel SIdorov (\u5317\u6d77\u9053\u5927\u5b66\u30fbICReDD) P-1\u00a0 \u00a0\u9ad8\u539f\u6e09 (NAIST) P-2\u00a0 \u00a0Sakthivel Balasubramaniyan (Hokkaido University) P-3\u00a0 \u00a0Said Byadi (Hokkaido University) P-4\u00a0 \u00a0Philippe Gantzer (Hokkaido University) P-5\u00a0 \u00a0Kaveri Prasad (Hokkaido University) P-6\u00a0 Gunther Stahl (OpenEye, Cadence Molecular Sciences) P-7\u00a0 \u00a0\u524d\u7530\u9678 (\u4e5d\u5dde\u5de5\u696d\u5927\u5b66) P-8\u00a0 \u5742\u53e3\u5927\u9580 (\u6a2a\u6d5c\u56fd\u7acb\u5927\u5b66) P-9\u00a0 \u9ad8\u4e0b\u5927\u8cb4 (\u65b0\u6f5f\u5927\u5b66) P-10\u00a0 Viejay Ordillo (NAIST) P-11\u00a0 MARTIN (Hokkaido University) P-12\u00a0 \u6749\u7530\u967d\u5f69 (NAIST) P-13\u00a0 \u65b0\u5ddd\u4e3b\u5b89 (NAIST) P-14\u00a0 \u6c34\u4e0a\u660c\u52c7 (NAIST) P-15\u00a0 Tang Xingyu (Hokkaido University) P-16\u00a0 Ruben STAUB (Hokkaido University) P-17\u00a0 \u5c71\u7530\u5927\u5922 (NAIST) P-18\u00a0 Mayumi Puspita (NAIST) P-19\u00a0 \u677e\u5143\u904a\u4eba (\u6a2a\u6d5c\u56fd\u7acb\u5927\u5b66\u5927\u5b66\u9662) P-20\u00a0 \u7fa9\u5247\u53f2\u5f25 (NAIST) P-21\u00a0 \u677e\u5c3e\u52c7\u4e8c\u90ce (\u682a\u5f0f\u4f1a\u793eTS\u30c6\u30af\u30ce\u30ed\u30b8\u30fc) P-22\u00a0 \u5c71\u672c\u5bcc\u4e5f (NAIST) P-23\u00a0 \u5c71\u53e3\u5fb9 (\u682a\u5f0f\u4f1a\u793eTS\u30c6\u30af\u30ce\u30ed\u30b8\u30fc) P-24\u00a0 \u6e21\u8fba\u822a\u4e5f (NAIST) P-25\u00a0 Milo Roucairol (Hokkaido University) P-26\u00a0 Supaporn Klabklaydee (Institute of Science Tokyo) P-27\u00a0 –<\/p>\n P-28\u00a0 Yusuke Tateishi (Kumamoto University) P-29 Aian B. Ontoria (NAIST)
\n“Chemical Language Models and Prediction Anatomy<\/a>“<\/em><\/p>\n
\n“AI\/ML approaches for generative chemical design and virtual screening acceleration<\/a>“<\/em><\/p>\n
\n“Data-driven drug search and design by machine learning<\/a>\u201d<\/em><\/p>\n
\n“DX platform for target discovery and drug design<\/a>“<\/i><\/p>\n
\n“Advanced 3D-Pharmacophores for Drug Discovery and De-Risking Neurotoxicity<\/a>“<\/em><\/p>\n
\n“Deep drug design: Advancing from hits to leads<\/a>“<\/em><\/p>\n
\n“QSPR modeling approaches representing conformations<\/a>“<\/em><\/p>\n
\n“Next Generation Pharmacophore Modeling: Novel Concepts & Tools for Molecular Design<\/a>“<\/em><\/p>\n
\n“Artificial Intelligence: The Future for Synthetic Chemistry?<\/a>“<\/p>\n
\n“Current in the data-driven synthesis route development methods in TS technology (DSRDM in TST) and its applications for fine chemicals<\/a>“<\/i><\/p>\n
\n“D-Tools: An Open Platform for Research and education in Chemoinformatics.<\/a>“<\/em><\/p>\n
\n“Chemography \u2013 from Abstract Mathematics to Library Design<\/a>“<\/em><\/p>\n
\n“Metrology informatics for electron microscopy<\/a>“<\/em><\/p>\n
\n“Materials Informatics for green energy harvesting and storage<\/a>“<\/em><\/p>\n
\n“Inorganic Materials Design Utilizing Machine Learning and Generative Adversarial Networks<\/a>“<\/em><\/p>\n
\n“Extended Molecular Representations for Electronic-Structure Informatics and Their Applications<\/a>“<\/em><\/p>\n\u4e00\u822c\u53e3\u982d\u767a\u8868\u8005\uff08\u30d7\u30ed\u30b0\u30e9\u30e0\u9806\uff09<\/h5>\n
\n“Quantitative structure\u2013reactivity relationship-guided mechanistic study of nitroxyl radical-catalyzed alcohol oxidation<\/a>“<\/em><\/p>\n
\n“Rapid evaluation of virtually synthesized compounds using a support vector machine model with reactant-wise kernels<\/a>“<\/em><\/p>\n
\n“Omics-based chemical structure generation considering molecular properties via deep reinforcement learning<\/a>“<\/em><\/p>\n
\n“The Full Mechanistic Tale of Pralidoxime in Acetylcholinesterase: Binding, Reaction, and Release<\/a>“<\/em><\/p>\n
\n“Bridging the Gap: Collaborative Tools for Chemoinformatics and Experimental Chemistry<\/a>“<\/em><\/p>\n\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u8005\uff08\u767a\u8868\u9806\uff09<\/h5>\n
\n“Domain Adaptation of Local LLMs for Metal-Sulfide Photocatalyst Discovery<\/a>“<\/em><\/p>\n
\n“Machine Learning-Driven Retention Time Prediction for High-Throughput Evaluation of Supercritical Chromatography Columns<\/a>“<\/em><\/p>\n
\n“Photoswitchable Wnt Pathway Agonists: Molecular Docking and Dynamics Reveal Selective Binding of cis-Isomer to Frizzled Receptors<\/a>“<\/em><\/p>\n
\n“Multicomponent Reaction Optimization with Gaussian Processes on Fragment Count Descriptors<\/a>“<\/em><\/p>\n
\n“Predictive Modeling and Experimental Validation of Azo-photo switches in Diverse Solvents<\/a>“<\/em><\/p>\n
\n“Extreme-Scale Search\u2026when Billions aren\u2019t Enough<\/a>“<\/em><\/p>\n
\n“Analysis of factors contributing to uncertainty in BCF predictions for chemical substances<\/a>“<\/em><\/p>\n
\n“Analysis of Asymmetric Reduction of Ketones Using Three-Dimensional Electronic States<\/a>“<\/em><\/p>\n
\n“Enhancing Generalization Performance of Molecular Property Prediction via Graph Latent Diffusion Autoencoder<\/a>“<\/em><\/p>\n
\n“Generative Modeling for Materials Design and Discovery using Invertible String-based Materials Representation<\/a>“<\/em><\/p>\n
\n“Developing Embedding Models for Cytochrome P450\u2013Ligand Interaction Scoring<\/a>“<\/em><\/p>\n
\n“Selective High-Precision Control of Process Variables in Automated Experimentation<\/a>“<\/em><\/p>\n
\n“Data assimilation model combined with symbolic regression for predicting band gaps of perovskite photocatalytic compounds.<\/a>“<\/em><\/p>\n
\n“Photocatalyst process informatics integrating experiment, simulation, and machine learning<\/a>“<\/em><\/p>\n
\n“Accelerating efficient reaction path search using NNP-AFIR method by pre-trained models<\/a>“<\/em><\/p>\n
\n“Solving the Passerini Reaction Controversy with Accelerated Reaction Path Search<\/a>“<\/em><\/p>\n
\n“Optimization of Copolymerization Conditions for Novel Copolymers Using Existing Polymerization Data and Physical Information<\/a>“<\/em><\/p>\n
\n“Distance-based applicability domain for screening Cu-based tantalate materials for visible-light-responsive photocatalyst candidates<\/a><\/em>“<\/em><\/p>\n
\n“Fingerprint-Informed Doc2Vec for Activity Prediction and Interpretation<\/a>“<\/em><\/p>\n
\n“Predicting the performance of doped hematite photoanodes incorporating the effects of lattice vibrations<\/a>“<\/em><\/p>\n
\n“Machine learning model for activation free energy and TS geometry of pericyclic reactions<\/a>“<\/em><\/p>\n
\n“Evaluation Framework for Thermodynamic and Chemical Validity in Generative Composition Models<\/a>“<\/em><\/p>\n
\n“User-Friendly Cloud System for Functional Chemical Synthesis Route Development<\/a>“<\/em><\/p>\n
\n“Development of an XRD Spectrum Generation Model for Crystal Structure Prediction Supporting Multi-site Substitution<\/a>“<\/em><\/p>\n
\n“Improving Retrosynthetic Planning With New Algorithms<\/a>“<\/em><\/p>\n
\n“Breaking the CRAM Behavior: AI-Generated Molecules Unlock Hidden Enzymatic Pathways in Marine Carbon Cycling<\/a>“<\/em><\/p>\n
\n“Towards Interpretable Prediction of SGLT2 Inhibitor Activity via Flexibility- and Electronic-Structure-Enhanced Graph Neural Networks<\/a>“<\/em><\/p>\n
\n“AI-Accelerated Discovery of High-Performance Thermoelectrics through Multi-Objective Optimization<\/a>“<\/em><\/p>\n