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Chemical simulation software - List of Manufacturers, Suppliers, Companies and Products

Chemical simulation software Product List

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[Case Study] Machine Learning Enabling Accurate Prediction of Precursor Volatility

[Case Study] Machine Learning Enabling Accurate Prediction of Precursor Volatility

Predict the evaporation or sublimation temperature with an accuracy of ±9°C on average, calculating hundreds of complexes per second.

A New Path to Precursor Development: Schrödinger's Machine Learning This predictive model opens a new avenue for designing new precursors with improved performance, optimizing not only the deposition and chemistry but also the temperature at which they can evaporate or sublime to be supplied as vapor. This advancement allows for a much broader range of structural changes to be screened computationally than before, enabling the generation of candidate precursors for experimental synthesis and testing that are less risky and more innovative. With this volatility model and the computational workflow for reactivity and decomposition based on Schrödinger's quantum mechanics, a complete design kit for vapor phase deposition and etching is provided, accelerating research on materials and processes for new technologies. *For 50 common metal and metalloid complexes, the evaporation or sublimation temperature at a given vapor pressure is predicted with an accuracy of ±9°C (about 3% of absolute temperature). *It can compute hundreds of complexes per second, resulting in a fast turnaround time. *For more details, please refer to the PDF document or feel free to contact us.

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  • MS_Maestro.png
  • LiveDesign.png
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[Information] Software that promotes the rapid and efficient development of new pharmaceuticals.

[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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  • simulator

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Challenging the structural prediction of unstructured epitopes and long CDR H3 loops.

Challenging the structural prediction of unstructured epitopes and long CDR H3 loops.

Application of replica exchange MD to the structural prediction of antigen-antibody complexes with long CDR H3 loops and disordered epitopes exceeding several dozen residues.

Schrödinger's drug discovery platform utilizes computational chemistry techniques based on the first principles of physics, enabling advanced drug design based on protein structural information, and is being utilized by major pharmaceutical companies worldwide. We will present the latest results of antigen-antibody simulations using our software at the seminar below. 22nd Annual Meeting of the Japanese Society for Protein Science Luncheon Seminar [Date and Time] June 7 (Tuesday) 12:00 - 12:50 Venue: Tsukuba International Conference Center 2F E Hall Session ID: LS1E [Program] Schrödinger's approach to physics-based antibody analysis and design: dealing with disordered epitopes and very long CDR H3 loop We will also have a booth at the corporate exhibition, so please stop by. You can experience Schrödinger's Biologics Modeling Suite: BioLuminate.

  • シュレーディンガー_セミナー要旨.jpg
  • antibody_GP120.png
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  • Other embedded systems (software and hardware)
  • Structural Analysis

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[Case Presentation] Panasonic New Design of Materials for Organic Electronics

[Case Presentation] Panasonic New Design of Materials for Organic Electronics

Panasonic and Schrödinger have designed over 50 new molecules that improve hole mobility.

Researchers at Panasonic are working on the novel development of organic semiconductor materials with high-efficiency characteristics. Panasonic is conducting joint research with Schrödinger, utilizing the high processing capabilities for DFT calculations, building machine learning/deep learning models, and enumerating chemical substances, leveraging the computational power and expertise provided by Schrödinger to achieve new designs of molecular materials. This catalog is a collection of case studies on "Novel Design of Hole-Conducting Molecular Materials for Organic Electronics," which Schrödinger has collaborated on with Panasonic. We invite you to read it. *For more details, please refer to the PDF document or feel free to contact us.*

  • Software (middle, driver, security, etc.)
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  • Organic EL

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[Case Study] Accelerating the Design of Organic EL Materials through Active Learning

[Case Study] Accelerating the Design of Organic EL Materials through Active Learning

High efficiency and cost performance! An active learning workflow that utilizes the synergy of physics-based simulations and machine learning for predicting optoelectronic properties.

Molecular modeling and simulation tools have been proven effective for materials discovery and are increasingly being adopted in industrial research and development. Digital simulation significantly reduces the time required in research and development workflows compared to traditional experimental approaches, but challenges remain. Schrödinger has made it easier to address these challenges. Recently, Schrödinger developed an active learning workflow that leverages the synergy between physics-based simulations and machine learning for predicting optoelectronic properties. Recent research by Schrödinger, published in Frontiers in Chemistry and presented at SID-Display Week 2022, demonstrates an active learning paradigm for the discovery of OLED materials. *For more details, please refer to the PDF document or feel free to contact us.*

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  • Organic EL

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High-Efficiency Compound Exploration Realized by FEP+

High-Efficiency Compound Exploration Realized by FEP+

Widely utilized in the field of chemistry, enabling cost reduction, efficient improvement of molecular profiles, and the exploration of highly accurate new compounds.

FEP+ is a technology based on the free energy perturbation method uniquely developed by Schrödinger. It enables the prediction of binding free energies between proteins and ligand molecules with reliability comparable to experiments across a wide chemical space. *For more details, please feel free to contact us.*

  • Embedded OS

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Free Webinar: Cosmetics Development Utilizing Digital Chemistry, February 19

Free Webinar: Cosmetics Development Utilizing Digital Chemistry, February 19

Physics-based simulation and machine learning software for a wide range of users, from beginners to experts in computational chemistry.

Schrödinger, Inc. will hold a webinar for materials science on February 19 (Wednesday) titled "Virtual testing of personal care and cosmetics formulations using digital chemistry methods." The development of sustainable products faces many challenges, requiring time, resources, and new raw materials. Predictive modeling is gaining attention to streamline this process. It allows for the identification of promising ingredients and formulations that meet standards, as well as new packaging materials, providing molecular-level understanding through virtual testing using computational methods. Specifically, it enables the analysis of the behavior of individual components, the form of formulations, stability, and interactions with biological surfaces. Additionally, it allows for the exploration of interactions between products and packaging materials, helping to identify factors that significantly affect shelf life. In this seminar, we will demonstrate through case studies how computational chemistry can assist in product development, container design, and analysis during product use. We invite you to join us without hesitation.

  • Embedded OS
  • Cosmetic materials and raw materials
  • Cosmetic synthesis and fermentation

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Promotion of Organic Electronics Materials Development

Promotion of Organic Electronics Materials Development

Efficient development of organic electronics materials using the integrated platform Materials Science Suite.

Organic electronics materials are required to have good optoelectronic properties and chemical stability as individual molecules, as well as desirable morphology and thermodynamic properties in the aggregated phase. The Materials Science Suite provides atomic-scale simulations applicable to these systems based on quantum chemistry, molecular dynamics, and machine learning, supporting efficient material development through the insights and theoretical interpretations obtained. *For more details, please refer to the PDF document or feel free to contact us.*

  • Other electronic parts
  • simulator
  • Organic EL

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[Presentation of Data] Improvement of Formulation Design Optimization through Coarse-Grained Molecular Simulation

[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.*

  • Embedded OS

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