From March 27 (Thursday) to March 29 (Saturday), we will exhibit and present at the 145th Annual Meeting of the Pharmaceutical Society of Japan.

シュレーディンガー

Schrödinger, Inc. will exhibit and present at the 145th Annual Meeting of the Pharmaceutical Society of Japan. Formulation research is one of the important processes in drug development, but in recent years, there has been a stronger demand for high efficiency and cost reduction. In this context, recent advancements in computers and molecular simulation technology have made it possible to predict physical properties, which was previously difficult to achieve in practice. In particular, our company is actively engaged in the development of software for formulation research as a leading company in molecular simulation software. 【Joint Symposium Date and Location】 March 27 (Thursday) 17:17 - 17:47 Venue: Room 4 (Fukuoka International Congress Center, 201 [2F]) Title: DX of Formulation Research Using Molecular Simulation and Machine Learning 【Exhibition Booth】 No.: 5F B-22 We look forward to your visit.

Date and time Thursday, Mar 27, 2025 ～ Saturday, Mar 29, 2025
08:30 AM ～ 05:00 PM
The last day is until 15:00.
Capital Fukuoka International Conference Center Marine Messe Fukuoka Building B
Entry fee Charge Registration is required.

Related product information

[Presentation of Data] Improvement of Formulation Design Optimization through Coarse-Grained Molecular Simulation
Understanding the mechanisms behind the dissolution reactions of amorphous solid dispersions (ASD) by the collaborative research team of AbbVie and Schrodinger.

Our computational chemistry platform is capable of addressing a wide range of materials research fields. ■ Property prediction through Density Functional Theory (DFT) calculations and first-principles calculations in periodic systems HOMO/LUMO/pKa/solvent effects/IR/Raman/UV-vis/VCD/NMR/oxidation-reduction potential/triplet excited state energy/TADF S1-Tx gap/fluorescence/phosphorescence/vibrational calculations/structure optimization/transition state calculations/reaction pathway analysis/adsorption energy/bond dissociation energy/electron and hole mobility/reorientation (rearrangement) energy ■ Property prediction using Molecular Mechanics (MM) methods, Molecular Dynamics (MD) methods, and coarse-grained MD Density/conformation analysis/cross-linked structures/Young's modulus/viscosity/surface tension/glass transition temperature (Tg)/molecular diffusion/thermal expansion/crystal morphology/swelling/stress-strain curves/solubility parameters Methods usable in machine learning Generation of various descriptors and fingerprints/Partial Least Squares (PLS) regression/multiple linear regression (MLR)/Principal Component Regression (PCR)/Kernel PLS/Bayesian classification/Recursive Partitioning (RP) analysis/Self-Organizing Maps/Tg, dielectric constant, boiling point, vapor pressure prediction models/genetic algorithms/active learning
[Presentation of Japanese Materials] Strengthening Research and Development with Industry-Leading Computational Chemistry Tools
[Japanese Flyer] Overview of Computational Chemistry Platform for Life Sciences, Drug Discovery, and Biotechnology

For more details, please feel free to contact us.
[Presentation of Japanese Materials] Enhancing the Precision and Speed of Material Development with High-Performance Computational Tools
[Japanese Flyer] Overview of Schrödinger's Materials Science Platform

Our Materials Science Suite is capable of addressing a wide range of materials research fields. ■ Property predictions using Density Functional Theory (DFT) calculations and first-principles calculations for periodic systems HOMO/LUMO/pKa/solvent effects/IR/Raman/UV-vis/VCD/NMR/oxidation/reduction potential/triplet excited state energy/TADF S1-Tx gap/fluorescence/phosphorescence/vibrational calculations/structure optimization/transition state calculations/reaction pathway analysis/adsorption energy/bond dissociation energy/electron and hole mobility/reorientation (rearrangement, reconfiguration) energy ■ Property predictions using Molecular Mechanics (MM) methods, Molecular Dynamics (MD) methods, and coarse-grained MD Density/conformation analysis/crosslinked structures/Young's modulus/viscosity/surface tension/glass transition temperature (Tg)/molecular diffusion/thermal expansion/crystal morphology/swelling/stress-strain curves/solubility parameters Methods available for use in machine learning Generation of various descriptors and fingerprints/Partial Least Squares (PLS) method/multiple linear regression (MLR)/Principal Component Regression (PCR)/Kernel PLS method/Bayesian classification/Recursive Partitioning (RP) analysis/Self-Organizing Maps/Tg, dielectric constant, boiling point, vapor pressure prediction models/genetic algorithms/active learning