Enhancing Efficiency, Empowering Decisions
In the fast-paced world of computational chemistry and drug discovery, efficiency and accessibility are key. At Cresset, we’re dedicated to empowering researchers with tools that not only enhance productivity but also simplify complex processes. Cresset’s AI is deeply integrated into our workflows, not just standalone tools. We focus on user-friendly interfaces that democratize access to advanced computational methods.
Cresset AI: Shaping the future of computational chemistry
Cresset AI combines trusted physics-based methods with cutting-edge machine learning to make drug discovery faster, smarter, and more accessible. From AI copilots that enhance usability, to generative chemistry for exploring novel chemical space, and validated ADMET predictions delivered through our consultancy, Cresset AI helps you innovate with confidence – while laying the foundations for future breakthroughs.
Our implementation of AI technologies is underpinned by our commitment to responsible AI which ensures transparency, fairness, and ethical use of AI models. Built with privacy and security by design, our solutions do not expose proprietary data to external services.
- AI Copilots to increase usability, enhance workflows and speed integrations
- Generative chemistry for creation of new drug-like molecules
- ADMET prediction with deployable, AI-driven custom ADMET models
- Advanced ML algorithms to accelerate and broaden computation
Enhancing User Experience with AI Assistants
We leverage AI to enhance productivity and empower our software users with actionable insights.
Within Flare™, our flagship computational chemistry platform, we are introducing new AI-powered assistants designed to elevate the user experience and make advanced computational tasks more accessible through low-code and no-code solutions.
The Flare Copilot helps users by answering questions from the product manual, covering everything from GUI elements to details on the underlying scientific tools. Our chatbots act as personal assistants inside Flare, guiding users through molecular modeling tasks in natural language. Our Coding Copilot automates PyFlare code writing, enabling researchers to create custom extensions without programming expertise. Together, these assistants streamline workflows and make sophisticated drug design tasks accessible to all researchers.
Enhancing User Experience with AI Assistants
- Navigate Flare's features with ease
- Streamline your scripting workflow
- Enhance your coding efficiency
Explore novel regions of chemical space with Generative chemistry
Complete integration of AI and deep-learning technologies throughout our medicinal chemistry solutions enables you to step beyond standard screening sets and explore untapped regions of chemical space. In the upcoming release of Flare, users will be able to generate new, diverse, drug-like molecules using AI.
MolGenAI, our generative chemistry integration layer in Flare, ensures a high degree of control for focused exploration. By utilizing Flare’s own scoring functions, you can optimize for properties previously evaluated in Flare, only generating molecules that are relevant and useful.
Cresset’s Gen-AI enables transfer learning to generate new molecules that are orientated to the chemical diversity of interest and re-train the model using in-house data to generate new molecules optimized for specific properties.
AI-generative chemistry combined with Cresset’s robust physics-based methods enable users to generate novel molecules, optimize existing molecules for better performance, predict properties and simulate behavior; all within a single solution.
AI ADMET property prediction: de-risk decisions
AI-powered in silico screening for ADMET properties flags risks early, ensuring only the molecules with the best chance of success are progressed. Predicting ADMET properties efficiently reduces the dataset to the hits that have real potential, focusing time and resources and avoiding progression of molecules that may present a liability further downstream.
ADMET prediction is currently available via Cresset’s Consultancy Services, where our team either trains new models with your datasets or deploys existing general models across different endpoints. When combined with de novo generative design, these predictions can effectively act as filters – prioritizing ideas that are more likely to be bioavailable and safe.
Cresset AI Strategic pillars
Leverage AI throughout our drug discovery lifecycle products and services
Leverage AI throughout our drug discovery lifecycle products and services, from design to development. Our approach combines generative AI assistants for more user-friendly interfaces, data-driven insights, and process optimization. By integrating AI into our digital transformation strategy, we enhance productivity, reduce compute costs, and empower researchers with actionable knowledge.
We prioritize Responsible AI by ensuring transparency, fairness, and ethical use of AI models
Compliance is crucial. We adhere to the EU AI Act, US AI Exec Order, US State AI, and China AI laws requirements; ensuring transparency, fairness, human oversight, and risk management. Our commitment to responsible AI ensures our tools are not only effective but ethical.
| Responsible AI category | Remedies and actions |
|---|---|
| Transparency | Disclose AI use to consumers and regulators |
| Provide opt-out mechanisms for AI-based decisions-making | |
| Be transparent about how AI-created content originated | |
| Fairness | Adopt processes to minimize biases in AI output |
| Human oversight | Include human-in-the-loop for higher risk processes |
| Document how systematic testing of AI systems is done | |
| Risk management | Require risk assessments for AI impact and use |
| Prohibit very high-risk use cases of AI | |
| Require impact assessment on fundamental individual rights | |
| Privacy | Monitor input of personal information |
| Mitigate privacy risks |
Developing and deploying AI solutions and features systematically
Developing and deploying AI solutions and features systematically (“DevOps for AI”) aligns with our digital transformation goals; enabling efficient, reliable, and scalable AI solutions.
Leveraging AI within-company improvements and optimisations
AI is not just for customer-facing products: within-company improvements and optimisations are crucial for enhancing productivity, streamlining processes, and ensuring seamless operations within the company.
Cresset's ecosystem of enterprise platforms enabling digital drug discovery
Computer Aided Drug Design (CADD) software
- Complete in silico solution to generate novel, active molecules with optimum efficiency
- Proven acceleration of small molecule discovery driving workflow efficiencies to discover new compounds faster
- Cost effective and easy to use software, making cutting-edge modeling techniques accessible to all
AI to accelerate new molecule discovery
- Complete in silico solution to generate novel, active molecules with optimum efficiency
- Proven acceleration of small molecule discovery driving workflow efficiencies to discover new compounds faster
- Cost effective and easy to use software, making cutting-edge modeling techniques accessible to all
Premier computational chemistry services
- Expertise and innovation to optimize across all modalities and target classes
- Gain access to experts in digital transformation for new molecule discovery
- Partnership from a team of experienced scientists with successful delivery of over 500 discovery projects
DMTA platform: digitize medicinal chemistry
- Cloud/SaaS solution to enhance CADD and digitize medicinal chemistry
- Collaboration platform to enable medicinal and computational teams to design and analyze new molecules together
