[Information] Software that promotes the rapid and efficient development of new pharmaceuticals.

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last updated：Oct 01, 2025

シュレーディンガー

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