Powerful Generative AI Innovation Accelerates Discovery of New Molecules

Abhinav Mehla and John Murray
August 24, 2023 | Updated: January 10, 2024

Since 2018, MongoDB and Google Cloud have collaborated to revolutionize the way companies interact with their data, providing an unrivaled experience in Google Cloud regions around the world through a strategic partnership. By delivering MongoDB's popular Atlas developer data platform and deep integrations with Google's data cloud to customers, the two companies are empowering businesses to create applications at scale with unprecedented data richness, all available through the Google Cloud Marketplace. This strategic partnership is bearing fruit. In the chemical industry, for example, users are now combining AI and data mining techniques using MongoDB Atlas with Google Clouds Foundation Models to accelerate the discovery of new molecules and make the process more environmentally friendly.

Check out our AI resource page to learn more about building AI-powered apps with MongoDB.

The next big step in generative AI

Developers can experience the powerful capabilities of MongoDB Atlas Vector Search and Google Cloud foundation models to quickly and easily build applications with AI-powered features to enable highly personalized and engaging end-user experiences. Vertex AI provides the text embedding API to generate embeddings from customer data stored in MongoDB Atlas. Atlas Vector Search is a fully managed service that simplifies the process of effectively indexing this high-dimensional embedding data within MongoDB and being able to perform fast vector similarity searches. This combined with Google’s PaLM can be used to create advanced functionality like semantic search, classification, outlier detection, AI-powered chatbots, and text summarization - enabling developers to quickly build and scale next-generation applications.

With Atlas Vector Search, developers can build intelligent applications powered by generative AI over any type of data. MongoDB Atlas Vector search combined with Google Cloud foundation models integrates the operational database, vector search, and LLMs into a single, unified, and fully managed platform. You can find out more about using Vector Search with Google Cloud foundation models from our information hub.

Accelerating the discovery of new molecules with generative AI

Every day brings new innovations in generative AI, and Vector Search is no exception. Developers are now using Google Cloud foundation models and MongoDB Vector Search to bring inventive applications to growing industries. In one example, MongoDB Partner Exafluence is utilizing AI and data mining techniques with MongoDB Atlas Vector Search and foundation models from Google Cloud to help joint customer Anupam Rasayan discover new molecules. India-based Anupam Rasayan is one of the leading companies engaged in the custom synthesis and manufacturing of specialty chemicals.

The new platform, called Exf ChemXpert, includes configurable components for a wide variety of applications in the chemical industry, such as property prediction to help design new molecules, chemical reaction optimization to make developing molecules more environmentally friendly, and novel drug discovery to develop new treatments in the pharmaceutical industry.

The home page for EXF ChemXpert, a one-stop platform that's accelerating the discovery of new molecules

According to Anand Desai, Managing Director of Anupam Rasayan, this powerful new integration shows great promise. "In the world of chemistry, LLMs are potential game-changers for day-to-day product research and optimization of reaction mechanisms and operations," Desai said. "It can speed up new product and process innovations, reduce usage factors and costs, and push R&D to new heights beyond conventional methods. This transformation driven by new generative AI-powered tools will bring in a shift from conventional perspectives and benefit the chemical industry at large."

In the Retrosynthesis Planner, the user asks a question about how to synthesize the chemical, acrylamide.

Next steps

Generative AI represents a significant opportunity for developers to create new applications and experiences and to add real business value for customers. For more about building applications on MongoDB Atlas with Google Cloud foundation models, including demos where you can see generative AI in action with MongoDB Atlas on Google Cloud, visit this special information hub.

To get started running MongoDB on Google Cloud, visit the Google Marketplace.

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Improving the Node.js Driver’s SCRAM-SHA-256 Support

MongoDB always strives to offer best-in-class features, functionality, and security. A number of authentication mechanisms currently exist to verify the identity of a connecting client to your cluster, and when using the Salted Challenge Response Authentication Mechanism (SCRAM) there are two possible hashing functions: SCRAM-SHA-1 and SCRAM-SHA-256 . The MongoDB Driver Authentication Specification outlines that when attempting to authenticate using SCRAM: “If SCRAM-SHA-256 is present in the list of mechanism, then it MUST be used as the default; otherwise, SCRAM-SHA-1 MUST be used as the default [...]”. A MongoDB Server ( mongos or mongod ) can be configured with a list of possible authenticationMechanisms . As a result, MongoDB can be configured to return new authentication mechanisms which can upgrade already running applications to more secure authentication. This is the case when SCRAM-SHA-256 is added to a cluster that previously only supported SCRAM-SHA-1 . Prior to hashing passwords with SHA-256, they will first be prepared using SASLprep . The MongoDB Node.js driver leverages an external library ( saslprep ) for this functionality, which was an optional dependency and only used if available. Though a number of checks were in place to ensure the library was available (and loaded), an edge case was found where these checks could fail and report availability incorrectly. Potential use Most applications won’t experience this issue, however if your Node.js project is being bundled using an alternate bundler (such as webpack ) it’s possible a variation of this issue may surface. If your application was affected, it would be unable to connect to your MongoDB cluster. The stack trace from the error that would be thrown should include a call to continueScramConversation similar to the following examples: { "errorType": "TypeError", "errorMessage": "saslprep is not a function", "stack": [ "TypeError: saslprep is not a function", " at continueScramConversation ([...]/index.js:xxx:yyy)", [...] ] } TypeError: l is not a function at continueScramConversation (/app/webpack:[...]/mongodb/lib/core/auth/scram.js:xxx:yy) Note that Mongoose applications can also be affected, as Mongoose wraps the Node.js driver: TypeError: (0 , o.saslprep) is not a function at continueScramConversation (/app/webpack:[...]/mongoose/node_modules/mongodb/lib/cmap/auth/scram.js:xxx:yy) Next steps The underlying issue was addressed in versions 5.7.0 , 4.17.0 and 3.7.4 of the MongoDB Node.js driver, so depending on the version of the driver being used by your application a minor version update will address this. Upgrading your application’s libraries and deploying to production may not always be possible in a timely fashion. If this is the case and you happen to hit the issue described above a workaround would be to append the authMechanism option to your connection string with a value of SCRAM-SHA-1 as follows: mongodb+srv://xyz.mongodb.net/test?authMechanism=SCRAM-SHA-1 This will force the driver to attempt authorization using the SCRAM-SHA-1 hashing algorithm. Note that connection string changes would still require the application to be restarted for those changes to take effect.

August 23, 2023

Next →

MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024