Virtual Twin Experiences for Drug Discovery
BIOVIA Contract Research Program for Drug Discovery
Generative AI-Enhanced 3D Virtual Twin Experiences for Drug Discovery
Human biology is inherently complex, and despite the buzz, artificial intelligence (AI) alone is not sufficient to solve the multifaceted optimization challenges of drug discovery. Real progress demands tangible results grounded in deep scientific understanding. BIOVIA’s approach integrates the capabilities of data-driven AI with orthogonal, time-tested methods, including physics-based simulation approaches.
What is the BIOVIA Contract Research Program?
The BIOVIA Contract Research Program for Drug Discovery is rooted in providing tangible results to our customers. We deliver novel, de-risked drug candidates optimized for pharmacological considerations that are highly likely to succeed in the wet lab.
Value of the BIOVIA Contract Research Program:
- $10M Potential Savings per Lead
- >50% accelerated outcomes
Combining GenAI with 3D Simulations for Drug Discovery
The BIOVIA Contract Research Program combines state-of-the-art methodologies, such as generative artificial intelligence (Gen AI) and physics-based modeling and simulation methods, to create tailored Virtual Twin Experiences (VTEs) at different stages of the drug discovery process. Our approach goes beyond traditional techniques by incorporating a “clinically aware” perspective starting from the beginning. These VTEs optimize lead candidates against a Target Product Profile (TPP) that captures the desired characteristics (including ADME and toxicity). The TPP is a foundational step, and the initial TPP can be fine-tuned based on the project evolution.
Small Molecule Therapeutics Design
In the context of small molecule therapeutics design, virtual experiments facilitate exploration of chemical space by generating novel molecular structures optimized for the specific biological target and desired drug characteristics. Once the synthesizable virtual molecules that meet the TPP criteria are identified, these molecules are further optimized using our proprietary orthogonal approaches to deliver novel, de-risked candidates.
Biotherapeutics Developability
Due to the complexity of biotherapeutic modalities, predicting key developability properties such as stability, aggregation, viscosity, and manufacturability remains a challenge in biotherapeutics. Our approach can address these challenges by simulating the effect of formulation from the beginning. As our VTEs are tailored to the TPP, our developed predictive models can be applied to the modality of interest in the selected formulations.
Let Us Collaborate
Our engagements are outcome-focused, confidential, and your team retains intellectual property on the results. Our expertise spans the entire drug discovery and development process. Let us start by discussing the therapeutic area of interest and the drug modality. If your team is interested in
- Accelerating complex drug discovery projects
- Achieving project-specific milestones faster
- Saving resources and time without compromising innovation
- Taking advantage of domain expertise in niche areas
- Accessing virtual twins that utilize best-in-class, advanced modeling and simulation, AI/machine learning, and data science solutions.
Contact us at BIOVIA.Services.CR@3ds.com to learn more.
Explore our Computational Functionalities
The Only Solution that Combines AI/Machine Learning, Physics-based 3D Molecular Modeling and Lab Informatics in a Single Environment
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FAQ - Virtual Twin Experiences for Drug Discovery
A Virtual Twin is more than just a digital replica of a molecule or biological system. It's a comprehensive computational model that incorporates real-world data, physics-based simulations and AI-driven predictions to accurately represent the behavior and properties of potential drug candidates throughout their development lifecycle. This allows researchers to test hypotheses and optimize compounds before physical synthesis.
Physics-based methods involve computational techniques that rely on the fundamental laws of physics to simulate and analyze complex systems. These methods often use mathematical models grounded in physical principles to predict how a system will behave under various conditions. They are commonly used in fields like drug discovery, materials science, engineering, and fluid dynamics, providing accurate insights into performance and behavior at an atomic level. These include quantum mechanics, molecular dynamics, particle dynamics, solvation thermodynamics, etc.
Generative AI accelerates drug discovery by designing new molecular structures, predicting drug-target interactions, and optimizing compounds faster than traditional methods. It enhances de novo drug design, explores novel chemical spaces, and improves predictive modeling, reducing costs and increasing success rates.
Combining Generative AI with physics-based methods enhances the capabilities of both approaches. GenAI can analyze vast datasets, identify patterns, and generate new designs or solutions. Physics-based methods ensure these outputs are feasible, reliable, and grounded in real-world physics. This combination leads to more efficient design processes, improved predictive accuracy, and the ability to explore broader solutions, ultimately accelerating innovation and development in various fields, including drug discovery.
Companies can engage with BIOVIA through customized contract research agreements. The process begins with discussing therapeutic areas, project goals, and required AI-driven modeling solutions. BIOVIA ensures a confidential, outcome-focused partnership where clients retain full intellectual property rights. Contact us at BIOVIA.Services.CR@3ds.com to learn more.
At its core, a digital twin offers a dynamic, data-driven reflection of a physical asset, primarily for monitoring and analysis. A virtual twin, however, represents a more advanced and interactive environment, enabling users to actively design, simulate, and optimize complex scenarios and entire lifecycles, moving beyond mere mirroring to predict and innovate.
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