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Company description

Cresset is a computational chemistry software and services company dedicated to accelerating drug discovery through AI-enriched simulation science. Built on over 20 years of computational chemistry innovation, Cresset combines cutting-edge science with artificial intelligence and machine learning to help discovery teams generate smarter ideas, make stronger decisions, and design better molecules — from target validation through to candidate selection.

Cresset serves a broad range of customers across the life sciences sector, from biotechs to large pharmaceutical organizations. The company provides both software solutions and expert discovery services, enabling computational and medicinal chemists to collaborate more effectively across the Design-Make-Test-Analyze cycle. Cresset's platform is trusted by the global scientific community, with its technology cited in peer-reviewed publications spanning medicinal chemistry, drug discovery, and in silico research.

Core Software Products

  • Flare™ — Cresset's leading digital molecular discovery solution for ligand- and structure-based design. Flare includes capabilities such as covalent docking, protein interaction potential visualization, and AI-driven enhancements. Version 11 introduced significant AI-powered efficiency gains to chemistry workflows.
  • Spark™ — A scaffold hopping and bioisosteric replacement tool that uses a field-based approach to identify novel substructures. Spark has enabled researchers to discover nanomolar hits and novel SAR that were not accessible through conventional methods.
  • FlareFEP — A free energy perturbation (FEP) module for predicting binding affinity. Active Learning FEP delivers over 80% savings in compute time and increases success rates by nearly 2.5x, making lead optimization faster and more reliable.
  • AI ADME — A property prediction tool that leverages AI to forecast ADME (absorption, distribution, metabolism, and excretion) characteristics of candidate molecules.

Key Capabilities and Scientific Approaches

  • Active Learning FEP — An advanced free energy perturbation workflow that combines active learning with FEP to dramatically reduce compute time while improving the success rate of lead optimization campaigns.
  • Large-Scale Virtual Screening — Cresset's combinatorial virtual screening capabilities have been applied to screen up to 200 million compounds, with successful identification of novel chemical classes not found through standard 2D fingerprint searches, leading to the synthesis of prioritized compound sets.
  • Field- and Shape-Based Methods — Proprietary electrostatic complementarity and field-based virtual screening methods, including those available through Blaze™, enable identification of novel chemotypes distinct from those arising from docking or pharmacophore approaches.
  • Electrostatic Complementarity™ — A proprietary technique used in virtual screening and ligand design to identify compounds with strong complementarity to target binding sites.
  • Protein Interaction Potential Visualization — Tools within Flare allow researchers to visualize common features across families of targets, informing ligand design in previously unexplored directions.
  • Scaffold Hopping and Bioisosteric Replacement — Spark's field-based approach enables researchers to replace key substructures in lead compounds with novel alternatives, driving the discovery of potent and structurally diverse analogs.

Discovery Services

  • Cresset Discovery provides expert computational chemistry services to support drug discovery projects at all stages, from hit identification to lead optimization.
  • Services include molecular modeling, field- and shape-based virtual screening, in silico design and prioritization, scaffold hopping, and binding pose elucidation.
  • Cresset Discovery has collaborated with organizations such as Epics Therapeutics to accelerate the optimization of METTL3 inhibitors, combining scaffold hopping and in silico design with medicinal chemistry expertise.
  • The team has supported projects including identification of backup series, putative binding pose determination consistent with kinetic mechanism of action, and SAR-driven ligand design for a range of therapeutic targets.

Notable Customers and Scientific Validation

  • Cresset's solutions are used by leading organizations including AstraZeneca, F2G, Cancer Research Technology Discovery Laboratories, PhoreMost, Confo Therapeutics, MAQU Chemistry Consulting, and Twentyeight-Seven Therapeutics, among others.
  • The platform has been cited in publications in the Journal of Medicinal Chemistry, npj Drug Discovery, Pest Management Science, and other peer-reviewed journals, covering applications ranging from RSV polymerase inhibitors to Alzheimer's disease research and fungal disease control.
  • Customers highlight Cresset's intuitive molecule design tools, powerful SAR analysis, novel structure generation, and the combination of software excellence with scientific support as key differentiators.

Deployment, Distribution, and Company Background

  • Cresset has been developing computational chemistry solutions for over 20 years, establishing an unrivalled platform for discovery organizations seeking scientific success.
  • The company operates as a responsible business, maintaining a secure and trusted provider status across its customer base, from small biotechs to large pharma.
  • Cresset solutions are available through direct sales in key territories as well as through a global distribution network, ensuring broad accessibility for international customers.
  • The company actively engages with the scientific community through workshops, conferences such as Drug Discovery Chemistry USA, and sponsorship of events such as the Alchemistry Workshop on Free Energy Methods in Drug Design.

With a strong foundation in rigorous computational science and a growing suite of AI-enriched tools, Cresset continues to lead the digital transformation of molecule design, helping discovery teams worldwide bring better drug candidates to the clinic faster and more efficiently.

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