Pharma Quest

Inspiration

The inspiration for Pharma Quest stemmed from a deep desire to accelerate the drug discovery process and make it more accessible to researchers worldwide. We recognized the challenges faced by scientists in sifting through vast amounts of data and the limitations of traditional methods. We were inspired by the potential of artificial intelligence and collaborative platforms to revolutionize this field and contribute to the development of life-saving medications.

What it does

Pharma Quest is an AI-powered platform designed to streamline the drug discovery process. It offers a comprehensive suite of tools and features, including:

1. AI-powered search engine: Allows researchers to quickly and efficiently search a vast database of molecules using advanced filters and AI-driven suggestions.

2. Molecule generation: Leverages the Nvidia MolMIM model to generate new molecules based on desired properties, significantly reducing the time and resources required for traditional methods.

3. Collaborative platform: Provides a space for researchers to connect, share findings, and work together in real-time using messaging and shared workspaces.

4. Data visualization: Utilizes interactive maps and visualizations to provide insights into molecular properties and relationships.

5. Integration with external resources: Connects with PubChem for comprehensive research data and RDKit for chemical informatics.

How we built it

Pharma Quest was built using a modern technology stack:

1. Next.js: Frontend framework for a seamless user experience and server-side rendering.

2. MongoDB: NoSQL database for scalable data storage and efficient querying.

3. Ably: Real-time messaging platform for instant communication and collaboration.

4. Nvidia MolMIM: AI model for generating new molecules based on desired properties.

5. RDKit: Open-source toolkit for cheminformatics and molecular modeling.

6. PubChem: Public database of chemical molecules and their activities.

We utilized a modular approach, breaking down the project into smaller components and focusing on code reusability. We also employed agile development methodologies to ensure efficient progress and rapid iteration.

Challenges we ran into

During the development of Pharma Quest, we encountered several challenges:

1. Integrating AI models: Incorporating the Nvidia MolMIM model required careful optimization and fine-tuning to ensure efficient performance and accurate results.

2. Real-time communication: Implementing seamless real-time messaging with Ably required overcoming latency issues and ensuring reliable data synchronization.

3. Data visualization: Creating interactive and informative visualizations required exploring different libraries and techniques to effectively represent complex molecular data.

4. Time constraints: The hackathon's limited timeframe required efficient time management and prioritization of features.

Accomplishments that we're proud of

Despite the challenges, we are proud of several accomplishments:

1. Successfully integrating AI into the drug discovery process: We effectively leveraged the Nvidia MolMIM model to generate new molecules, demonstrating the potential of AI in this field.

2. Building a collaborative platform: We created a space for researchers to connect and work together, fostering a sense of community and shared purpose.

3. Developing a user-friendly interface: We designed an intuitive and accessible interface that makes complex data and tools easy to navigate and use.

4. Completing a functional prototype within the hackathon timeframe: We successfully delivered a working prototype that showcases the core features and potential of Pharma Quest.

What we learned

Throughout the development process, we gained valuable knowledge and experience:

1. Working with AI models: We deepened our understanding of AI and its applications in drug discovery.

2. Real-time communication technologies: We gained practical experience with Ably and learned how to implement real-time features in web applications.

3. Data visualization techniques: We explored various libraries and approaches to effectively represent complex data.

4. Collaborative development: We honed our teamwork and communication skills, learning how to effectively collaborate on a challenging project.

What's next for Pharma Quest

We envision a bright future for Pharma Quest, with plans to:

1. Expand the molecule database: Incorporate more diverse and comprehensive datasets to provide researchers with a wider range of options.

2. Enhance AI capabilities: Explore and integrate additional AI models to further accelerate the drug discovery process.

3. Develop advanced analytics: Provide researchers with deeper insights into molecular properties and relationships through advanced analytics and reporting tools.

4. Build partnerships: Collaborate with research institutions and pharmaceutical companies to further develop and deploy Pharma Quest in real-world settings.

5. Community building: Foster a vibrant community of researchers by organizing workshops, webinars, and other events.

We believe that Pharma Quest has the potential to revolutionize the drug discovery process and contribute to the development of life-saving medications for a healthier tomorrow.

Built With

• ably
• express.js
• map
• mongodb
• next.js
• nextauth
• nvidia
• pubchem
• rdkit
• typescript
• vercel