Dynamo Software
Unlock precision and clarity in alternative investments with Dynamo Software, a cloud-native, AI-powered platform that unifies your entire workflow. We provide a single, configurable solution for your front-, middle-, and back-office needs.
For General Partners (GPs), Dynamo enhances every stage of the investment lifecycle with advanced CRM, deal pipeline tracking, fundraising tools, and secure investor relations and fund accounting reporting.
For Limited Partners (LPs), our platform delivers real-time research and portfolio management capabilities. We automate document ingestion, data extraction, and holdings enrichment, providing deep exposure analytics for informed decision-making.
Dynamo serves a wide range of private capital firms, including private equity, venture capital, real estate, hedge funds, and infrastructure. Our platform is also tailored for endowments, pensions, foundations, family offices, fund of funds, and fund administrators. By centralizing all investment data into a single source of truth, we equip your team with the control needed to uncover powerful insights.
Our AI-driven system automates data ingestion and tagging, while our HoldingsInsight feature enriches portfolio data for advanced analysis. All modules work together seamlessly, supported by a dedicated Client Services team committed to your success. With Dynamo, you can streamline operations, improve data accuracy, and drive strategic decisions with confidence.
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Teradata VantageCloud
Teradata VantageCloud: Open, Scalable Cloud Analytics for AI
VantageCloud is Teradata’s cloud-native analytics and data platform designed for performance and flexibility. It unifies data from multiple sources, supports complex analytics at scale, and makes it easier to deploy AI and machine learning models in production. With built-in support for multi-cloud and hybrid deployments, VantageCloud lets organizations manage data across AWS, Azure, Google Cloud, and on-prem environments without vendor lock-in. Its open architecture integrates with modern data tools and standard formats, giving developers and data teams freedom to innovate while keeping costs predictable.
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SYNTHIA Retrosynthesis Software
SYNTHIA™ Retrosynthesis software, developed by computer scientists and coded by chemists, allows scientists to quickly and easily navigate novel and innovative pathways for novel and previously published target molecules.
You can quickly and efficiently scan hundreds pathways to identify the best options for your needs.
Discover the most cost-effective route to your target molecule with the latest visualization and filtering features.
You can easily customize the search parameters to eliminate or highlight reactions, reagents, or classes of molecules.
Explore innovative and unique syntheses to build your desired molecule.
Easy to generate a list for starting materials that are commercially available for your synthesis.
ISO/IEC 27001 Information Security Certification will guarantee the confidentiality, integrity and protection of your data.
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Aurora Drug Discovery
Aurora utilizes principles of quantum mechanics and thermodynamics alongside a sophisticated continuous water model to assess the solvation effects on ligand binding affinities. This methodology is significantly different from the traditional scoring functions typically employed for predicting binding affinities. By integrating entropy and aqueous electrostatic contributions directly into the computations, Aurora's algorithms yield far more precise and reliable binding free energy values. The interaction between a ligand and a protein is fundamentally defined by the binding free energy value. This free energy (F) serves as a thermodynamic measure that correlates directly with the experimentally determined inhibition constant (IC50), influenced by factors such as electrostatic interactions, quantum effects, aqueous solvation forces, and the statistical characteristics of the molecules involved. Non-additivity in F arises primarily from two key components: the electrostatic and solvation energy, and the entropy, which together contribute to the complexity of ligand-protein interactions. Understanding these contributions is essential for the accurate prediction of binding affinities in drug design and molecular biology.
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