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[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.

Executive Summary - Evaluation of dissolution profiles for various combinations of drugs and polymers under specific conditions - Identification of interactions causing release delays in specific formulations - Cohesive complementary experimental data through molecular-level visual and numerical insights - Insights gained regarding new excipients for formulation compositions to achieve target solubility *For more details, please feel free to contact us.*

Related Link - https://www.schrodinger.com/life-science/solutions…

basic information

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

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