[Information] Software that promotes the rapid and efficient development of new pharmaceuticals.
We promote the rapid and efficient development of new drugs through physics-based modeling and simulation, along with automated workflow solutions.
As the speed of drug discovery accelerates, the rapid and efficient preformulation and formulation of new drugs has become a crucial element in pharmaceutical development. Advances in atomic-scale modeling and simulation techniques have made it possible to conduct in silico screening of numerous candidate materials and formulations based on complete physics-based models. [Case Studies] - Stability of drugs against chemical degradation - Compatibility of pharmaceutical ingredients - Thermophysical stability based on glass transition temperature - Controlled release: Supramolecular structures in formulation *For more details, please refer to the PDF document or feel free to contact us.
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Our computational chemistry platform is capable of addressing a wide range of materials research fields. ■ Prediction of physical properties 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, reconfiguration) energy ■ Prediction of physical properties using Molecular Mechanics (MM), Molecular Dynamics (MD), and Coarse-Grained MD Density/conformation analysis/crosslinking structure/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) 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
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For more details, please refer to the PDF document or feel free to contact us.
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Schrödinger Co., Ltd. is the Japanese subsidiary of Schrödinger Inc., headquartered in New York, USA. Schrödinger has a history of about 30 years in developing software that integrates advanced technologies in chemistry and computer science, primarily in the fields of materials science and life sciences, providing advanced solutions for drug discovery, biologics, and materials research and development.