Small Molecule Therapeutics Design
The Only Solution that Combines AI/Machine Learning, Physics-based 3D Molecular Modeling and Lab Informatics in a Single Environment
Identify High-Quality Small Molecule Drugs Faster
Drug discovery requires a multidisciplinary approach. BIOVIA’s end-to-end integrated solutions for Small Molecule Therapeutics Design are built over 30 years of expertise in research informatics, physics-based molecular modeling and simulation, and lab informatics. They facilitate collaboration across multidisciplinary research teams to design, discover and develop small molecule drugs faster at reduced costs.
One Platform: Flexible, Scalable and Extensible Solutions on Cloud
BIOVIA's solutions for Small Molecule Therapeutics Design are available as Software-as-a-Service (SaaS) applications, seamlessly integrated on the 3DEXPERIENCE platform. They can accommodate the research needs of both small teams and large pharmaceutical organizations, with the ability to:
- Quickly deploy the newest solutions without hardware or IT investment
- Customize data-driven workflows focused on unique requirements of individual research projects
- Incorporate unique custom or third-party models, algorithms, and data sources.
Highly Accurate Predictive Modeling with Active Learning Cycles
BIOVIA streamlines drug discovery by seamless integration of virtual lead identification and optimization methods with your own experimental design/testing in the lab:
- Make predictions on “what to make next” using “active learning”
- Democratize access to predictive models for increased efficiency
- Ensure data integrity, security, traceability, and consistency throughout the drug development lifecycle.
State-of the-art Science with AI and Machine Learning
- AI-driven Drug Design
- Modeling and Simulation
- Model Building
Reduce expensive real-world testing with generative AI
BIOVIA Generative Therapeutics Design (GTD) automates the virtual creation, testing and selection of novel small molecules with generative AI to design. It improves lead quality using multiparameter optimization (MPO) strategies that simultaneously address complex target product profiles (TPPs).
Researchers use this intuitive AI-based workflow to shorten development timelines, discover obscured insights, explore chemical space and save millions of research dollars per program. GTD is the only commercial-grade software designed to empower computational and medicinal chemists with AI for small molecule drug development.
Combine AI and physics-based modeling for drug discovery
BIOVIA Discovery Studio Simulation allows molecular modelers to build 3D pharmacophore and docking models, which then can used in BIOVIA Generative Therapeutics Design for enhancing the quality of the lead compounds. Users also have access to the premier high throughput docking algorithm GOLD from the Cambridge Crystallographic Data Centre (CCDC) with no additional license requirement.
Discovery Studio Simulation now includes OpenFold/AlphaFold AI models for structure prediction, in addition to validated physics-based, long-validated homology modeling algorithm MODELER.
Build ML models easily, with your own lab data
Providing an easy to use, interactive user interface, BIOVIA Machine Learning Workbench facilitates high-quality model building for researchers with little or no data science background. These models are then used in BIOVIA Generative Therapeutics Design experiments to optimize lead compounds.
Orchestration of model training on a recurrent basis or when new data becomes available helps identify the best drug candidates, leveraging all assay data for improved predictive modeling.
Harnessing the Power of AI-Ready Data
- Data Insights
- Connected Data
Gain Insights from Data
Make fast, informed decisions on which compounds to synthesize for testing.
With BIOVIA Insight, scientists can visualize, analyze, and browse molecular data proposed by Generative Therapeutics Design in detail, together with calculated properties and experimentally derived data, and can compare chemical structures, properties or other custom visualizations, while collaborating seamlessly with other team members.
Connect to Real World Data
BIOVIA Scientific Notebook and BIOVIA Materials Registration help improve efficiency and team productivity by leveraging existing knowledge, universal representation of materials, and bridging the gap between discovery and downstream activities.
They support many steps in Small Molecule Therapeutics Design, including:
- The planning of experiments
- The registration of virtual compound ideas
- The capture of chemical reactions and synthesized compounds as lots
- Determining whether the virtually designed compounds already exist as real compounds in team’s inventory
Start Your Journey
The world of Small Molecule Therapeutics Design is changing. Discover how to stay a step ahead with BIOVIA
FAQ About Small Molecule Drugs
The primary difference between large and small molecule drugs lies in their size, structure, production process, and how they function within the body. Below is a comparative table that highlights key differences between large and small molecule drugs:
| Feature | Small Molecule Drugs | Large Molecule Drugs (Biologics) |
|---|---|---|
| Size | Small (usually less than 1 kDa). | Large (typically > 1 kDa, up to 150 kDa or more). |
| Structure | Simple, well-defined chemical structure. | Complex, three-dimensional structure. |
| Production | Synthesized chemically in laboratories. | Produced using living cells (biotechnological processes). |
| Stability | Stable, can often be taken orally. | Less stable, usually requires refrigeration and injection. |
| Route of Administration | Oral, topical, intravenous, etc. | Mostly injectable (intravenous, subcutaneous). |
| Delivery Mechanism | Can easily enter cells due to small size. | Targets extracellular components due to large size. |
| Target | Often acts on intracellular targets like enzymes or receptors. | Primarily targets extracellular receptors or proteins. |
| Immunogencity | Low potential for triggering immune response. | Higher potential for immunogenicity. |
| Metabolism | Metabolized in the liver, usually by enzymes. | Broken down by proteolysis, primarily in cells or bloodstream. |
| Cost of Development | Typically lower production and development costs. | High production costs due to complex manufacturing. |
| Patent Lifespan | Generally shorter due to simpler production. | Longer due to complex development and regulation. |
| Examples | Aspirin, Ibuprofen, Statins, Metformin. | Insulin, Monoclonal antibodies (e.g., Rituximab), Vaccines. |
Examples of small molecule drugs include:
- Aspirin (acetylsalicylic acid) - Used as a pain reliever, anti-inflammatory, and antiplatelet agent.
- Ibuprofen - A nonsteroidal anti-inflammatory drug (NSAID) commonly used to relieve pain, reduce inflammation, and lower fever.
- Paracetamol (acetaminophen) - Used as a pain reliever and fever reducer.
- Simvastatin or Atorvastatin - Medications used to lower cholesterol levels in the blood.
- Metformin - An oral medication used to treat type 2 diabetes by lowering blood sugar levels.
- Omeprazole - A proton pump inhibitor used to reduce stomach acid production and treat conditions such as gastroesophageal reflux disease (GERD) and peptic ulcers.
- Ciprofloxacin - An antibiotic used to treat bacterial infections.
- Sertraline - An antidepressant medication belonging to the selective serotonin reuptake inhibitor (SSRI) class.
- Warfarin - An anticoagulant medication used to prevent blood clots.
Small molecule drugs offer several advantages, including:
- Oral administration
- Cell membrane permeability
- Stability
- Manufacturing ease
- Target specificity
- Bioavailability
- Fewer drug interactions
Unlike small molecule drugs, which are synthesized chemically, large molecules are created using biological processes.. These biologics are manufactured using living cells, which makes the process more complex, expensive, and time-consuming. They often require special conditions for storage and administration (like refrigeration and injection).
AI improves small molecule therapeutics design by automating the exploration of chemical spaces and predicting molecular interactions. This enables researchers to identify promising compounds faster, reduce trial-and-error testing, and optimize lead molecules with better precision.
BIOVIA’s platform enhances small molecule drug development by integrating AI, machine learning, and molecular modeling to streamline the discovery process. Researchers can make data-driven decisions, optimize lead compounds, and accelerate the overall development timeline.
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