Viva Biotech Launches the AI-Driven Drug Discovery Platform, Transforming New Drug R&D Logic, Enabling One-Stop Innovative Drug Discovery

(Watch the full launch event highlights here)
SBDD Platform Powered by AI and Discovery Magic
As a one-stop drug discovery solution provider,
A New Paradigm in Drug Discovery: Breaking Time and Cost Barriers
New small molecule design workflow
In traditional small molecule drug discovery, the process of advancing a candidate to the clinical stage typically takes 2-4 years. This long timeline is largely due to the sequential approach of optimizing one property at a time, which often requires revisiting previous steps. However, with the advent of AI-driven workflows, the timeline is drastically reduced to under 1-2 years, with fewer synthesis and assay cycles. Virtual screening and de novo design are now indispensable steps in project initiation, followed by rapid iterations of molecular generation and experimental validation. These iterations occur within just a few weeks, allowing for parallel tracking of multiple properties. As a result, drug candidates progress to the lead optimization stage much more quickly and are further refined through an AI-guided design-make-test cycle. This new approach is 2-3 times faster, reducing total development costs by 50-70% as the project advances to the PCC stage.
New Biologics Design Workflow
Three Powerful Modules Shaping a New Era of AI-Driven Drug Discovery
For the first time,
V-Scepter
V-Scepter includes pocket identification, force field parameterization, molecular docking, ADMET predictors and series of other useful tools.
V-Orb
V-Orb consists of active-learning augmented virtual screening as well as the molecular dynamics. Built on the enhanced sampling techniques, are our proprietary Free Energy Perturbation (FEP) calculation suits, for non-covalent, covalent binders, and biologics.
V-Mantle
V-Mantle encompasses large language models as the foundation for feature extraction and downstream tasks. De novo design and complex structure prediction are the key pillars that form a network of the state-of-the-art AI-driven drug discovery. Lastly, the antibody engineering platform represents a streamlined process with accuracy and automation.
Through a series of live demonstrations, the platform's powerful capabilities in accelerating drug discovery is further demonstrated. One highlight was the ADMET prediction tool, which delivered highly consistent results with the experimental data, underscoring the reliability and accuracy of the underlying models. Another case focused on enhanced Molecular Dynamics (MD) and FEP, illustrating how the team overcame limitations of traditional computational frameworks. The demonstration emphasized the coupling between dynamic sampling and Quantum Mechanics (QM) to enable covalent interaction optimization. The entire process brings together efficiency and accuracy in a seamless workflow.
In the field of antibody design,
- Structure prediction
- Large language model as the foundation model of feature extraction and epitope prediction
- De novo design tools to generate antibody sequences from scratch
- Iterative design cycles to improve the affinity
- A comprehensive set of antibody developability index prediction tools to enhance the full drug profile
In the end,
We firmly believe that the integration of data, algorithms, molecular structures, biological mechanisms, and both computational and experimental workflows is key to unlocking the full potential of AI in drug discovery. By uniting cutting-edge computational modeling with rigorous experimental validation, the company's AI platform is transforming the once-impossible into achievable breakthroughs.
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