シュレーディンガー Product Lineup

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.*

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.

We will introduce the features of Schrödinger's Materials Science Solutions (MSS) in an easy-to-understand manner. 【Product Features】 ■ Molecular design using quantum calculations ■ Prediction of liquid and polymer physical properties ■ Crystals, surfaces, and interfaces: First-principles calculations for periodic systems, chemical reactions on electrodes and catalysts, and a wide range of applications to semiconductors/molecular crystals/MOFs ■ Statistical analysis and machine learning ■ Flexible and powerful GUI/CUI user interface *For more details, please feel free to contact us.

Our Materials Science Suite is capable of addressing a wide range of materials research fields. ■ Property predictions through 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 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

Schrödinger provides a software platform for innovation in the development of diverse materials, including polymer materials, organic electronics, catalysis and reactivity, thin film processes, energy recovery and storage, pharmaceutical formulations, consumer goods, metals, alloys, and ceramics. By exploring vast compound spaces and predicting molecular properties with high precision, it supports the rapid design of new materials and enhances cost efficiency. This document provides an overview of the platform for materials development. *For more details, please feel free to contact us.*

A leading software platform for molecular discovery and optimization for innovators in the biotechnology and pharmaceutical industries. It supports the design of highly efficient new therapeutics through the exploration of vast compound spaces and high-precision predictions of molecular properties. *For more details, please feel free to contact us.*

Schrödinger's advanced physics-based modeling and AI/machine learning software supports the development of a wide range of materials, including polymer materials, batteries, semiconductors, organic electronics, catalysts, pharmaceutical formulations, cosmetics, metals, alloys, and ceramics.

We offer a wide range of modeling services to accelerate and support your projects at each stage of the drug discovery process. - You can leverage Schrödinger's latest technology on a large scale. - You can utilize the expertise of our computational chemistry specialist team. - This includes all licenses, computational environments, and service hours. ■ Target Functionalization Services We unlock the potential of programs to enable structure-based rigorous drug design. ■ Hit Discovery Services Using the latest virtual screening technologies, we quickly identify a more diverse range of hit compounds. ■ De Novo Design Services We rapidly generate and prioritize novel design ideas that meet project-specific conditions. ■ Structure-Based ADMET Services Through structure-based design, we more efficiently reduce risks related to ADMET. ■ Crystal Structure Prediction Services By identifying the most stable polymorph at room temperature, we reduce risks associated with solid form selection.

We support drug discovery programs by leveraging Schrödinger's unique technology and expertise. By utilizing our experts along with the latest large-scale virtual screening and rigorous scoring techniques, we lead hit discovery to success. ■ Maximize the use of Schrödinger's hit discovery capabilities There is no need for initial investments in licenses or hardware. All computational resources, licenses, and working time necessary for cutting-edge hit discovery are included in the service. ■ Acquire a greater number of high-quality hit compounds with our unique scoring technology ■ Maximize novelty and diversity through screening billions of compounds ■ Enable FEP+ execution from structural information at any level Whether starting from template-based homology models, AlphaFold models, incomplete cryo-EM structures, or experimental X-ray structures, we provide the scientific insights and computational resources needed to address your unique projects. *For more details, please refer to the catalog.

Save time and resources—leave the preparation of target protein-ligand systems for structure-based drug discovery to us. Whether starting from X-ray structures, cryo-EM structures, AlphaFold predicted structures, or homology models, Schrödinger's scientists will utilize their unique technologies and expertise to optimize and validate protein-ligand complexes for highly accurate structure-based design. ■ Create designable structures that are on-target or off-target ■ Complement your unique insights on targets with our computational chemistry Schrödinger provides extensive expertise gained from addressing challenging targets in structure-based drug discovery using FEP+. ■ We can accommodate starting from template-based homology models, AlphaFold models, incomplete cryo-EM structures, or experimental X-ray structures. *All computational resources, licenses, and working hours necessary for comprehensive evaluation of target proteins and the construction of accurate structural models are included in the service. *For more details, please refer to the catalog.

- Maximize the use of Schrödinger technology: The service includes all necessary computational resources, licenses, and working hours to conduct comprehensive simulations tailored to research and development needs. - Provide flexible and customizable solutions for project success: Your software, hardware, and computational resources are not required. During and after the project, you will receive knowledge transfer and training from our experts. We design and provide tools and solutions according to your objectives. - Complement your expertise with Schrödinger's specialized capabilities: After jointly defining the scope of deliverables, experts in materials applications and digital simulations will carry out the project.

Machine learning force fields, also known as "machine learning interatomic potentials," have emerged as an important tool for achieving cost-effective atomic-level simulations of diverse chemical systems, often achieving accuracy comparable to density functional theory (DFT) at significantly lower computational costs. Recent advancements in message-passing networks have overcome the challenge faced by traditional MLFFs of being limited in the types of elements they can accommodate. Furthermore, the introduction of atomic charges and electrostatic interactions using charge equilibration methods allows for precise reproduction of multiple charge states, ionic systems, and electronic response characteristics, achieving even higher accuracy by explicitly considering long-range interactions. Our MLFF architecture, "MPNICE," incorporates explicit electrostatics for accurate charge representation. We also provide a set of pre-trained models trained on materials covering the entire periodic table (89 elements). MPNICE emphasizes high throughput performance, enabling long-duration and large-scale atomic-level simulations that were difficult to achieve with traditional methods while maintaining high accuracy.

The use of AI in materials exploration is becoming a global standard; however, there are still many cases where implementation is not progressing due to reasons such as "difficult to operate" and "lack of data science personnel." Schrödinger has made a webinar video (with Japanese subtitles) available for free to address these challenges. You can watch it immediately after registering through the form. ▼ You can watch it right away by registering through the form below ▼ https://info.schrodinger.com/ms-psb-01-submit-form In the video, we demonstrate how to perform advanced property predictions and machine learning-based experimental design (ML-based DoE) using Schrödinger's materials development simulation platform, 'Materials Science Suite,' even without specialized knowledge. We will introduce practical examples, such as approaches to derive "the next experiment to conduct" from small data sets, viscosity predictions of binary mixtures, and optimization of complex multi-component formulations. First, experience the acceleration of research and development through the use of AI in the video.