Our Impact
Explore the success stories, client collaborations, and scientific contributions that highlight our profound impact across the industry.
Customer Testimonials
"I am delighted to share that CSIR-CDRI and Sravathi AI have been collaborating on a target of common interest in the area of cancer. Sravathi’s expertise in applying artificial intelligence to drug discovery complements CDRI’s strengths in biology and chemistry. Our goal is to jointly accelerate the identification of novel, effective, and safe cancer therapeutics."
"I had one of the best customer service experiences with Sravathi AI Technologies Ltd. From the moment I reached out, the representative was friendly, attentive, and truly committed to resolving our Domain related issue. Not only did they listen carefully to my concerns, but they also followed up promptly with updates and went above and beyond to make sure I was satisfied. It’s rare to find a team that genuinely cares about its customers, but this one exceeded all my expectations. I left the interaction feeling valued, respected, and confident in continuing to do business with them. Truly a gold standard in customer care!"
“Our collaboration with Sravathi AI has been highly productive and professional. Their team brings strong scientific expertise and an impressive capacity for execution, consistently advancing novel compounds across multiple oncology targets. Through structured biweekly meetings, we have observed steady progress, including the successful synthesis and delivery of approximately ten compounds for testing. We value their rigor, responsiveness, and commitment to high-quality science, and view Sravathi AI as a trusted partner in our translational research efforts.”
Case Studies & Publications
Case Study : AI-Guided Optimization of ADC Payload Potency to Reduce Toxicity
High-potency payloads can make antibody-drug conjugates (ADCs) highly effective, but they often come with significant off-target toxicity. This case study shows how we used an AI-driven approach to solve this common problem. By analyzing preclinical data and employing AI-powered predictive models, we redesigned an ADC payload to intentionally lower its potency. The result was a safer therapeutic with a wider therapeutic window and preserved antitumor efficacy.
Case Study : AI-Driven Agrochemical Discovery
To address the urgent challenge of crop losses from pests, we've developed an AI-plus-physics-based computational pipeline to accelerate the discovery of new, sustainable pesticides. By leveraging deep learning and molecular modeling, we can explore millions of virtual molecules, design novel compounds, and predict high potency and safety, dramatically reducing the time and cost of traditional agrochemical R&D
Identifying Unknown Impurity Formation
Ensuring drug purity and safety requires anticipating impurities before they appear in the lab. This case study showcases how our Chemistry AI platform applies predictive modeling to identify unknown impurities and their formation pathways early in development, enabling proactive process adjustments that save time, reduce risk, and ensure higher-quality outcomes.
Identifying Rxn conditions for Atazanavir
This case study demonstrates how our platform was used to optimize the Suzuki coupling reaction for a key intermediate of the drug Atazanavir. The goal was to identify the optimal reaction conditions to achieve the highest possible yield.
Quantum Calculations for Feasibility of Routes of Synthesis
In modern drug development, choosing the right synthetic route is crucial. Using our Chemistry AI platform powered by quantum calculations, we can predict reaction feasibility upfront, compare pathways, and identify the most promising route. This approach streamlines decision-making, reduces trial-and-error, and accelerates experimental validation.
Optimization Through Number of Steps and Available Raw Materials
This case study highlights how our platform was used to optimize the synthesis of the drug Nilotinib. It demonstrates the power of AI in transforming a complex, multi-step process into a streamlined, commercially viable route with fewer steps and more readily available starting materials. The our Chemistry AI platform, unlike traditional methods, rapidly identifies and optimizes multiple synthetic routes simultaneously.
AI-Driven Discovery of a First-in-Class Candidate for Pancreatic Cancer
Background: Pancreatic cancer is highly aggressive and treatment-resistant, with limited options and poor survival rates. Traditional drug discovery has made limited progress, underscoring the need for innovative approaches.
Quantum-Chemical Screening of Active Components for Next-Generation FMCG Applications
At Sravathi AI, By integrating Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations, we uncover molecular-level insights that guide the rational design of cosmetic ingredients and formulations. This approach moves beyond conventional trial-and-error experimentation, enabling predictive, science-backed strategies that improve product performance, stability, and safety.
AI-Driven Repurposing of a Phase 3 Failed Compound for Pancreatic Cancer
A small-molecule compound that had previously failed in Phase 3 trials for a non-oncology indication presented an opportunity for repurposing due to its favorable safety profile and drug-like properties. Given the urgent need for new treatments in pancreatic cancer, we applied our AI- and physics-based platform to identify new potential oncology applications for the molecule.
Molecular Glue-Design-Evaluator (MOLDE): An Advanced Method for In-Silico Molecular Glue Design
This research introduces the Molecular Glue-Design-Evaluator (MOLDE), an innovative computational method designed for the rational design of molecular glues. By using a combination of techniques, including new chemical entity generation, optimization, and molecular dynamics simulations, MOLDE aims to accelerate the discovery process and pave the way for targeting previously inaccessible proteins.
PROTAC-Design-Evaluator (PRODE): An Advanced Method for InSilico PROTAC Design
Our research introduces the PROTAC-Design-Evaluator (PRODE), an advanced computational method for the in-silico design of these complex molecules. This innovative approach allows us to rapidly and effectively design PROTACs for new systems, such as the FGFR1-MDM2 complex, offering a promising path toward new therapeutic strategies.
News
Sravathi AI Crowned Winner of L'Oréal Big Bang 2025 SAPMENA Final
We are incredibly proud to announce that Sravathi AI has been declared the official winner of the prestigious L'Oréal Big Bang 2025 SAPMENA Final! This monumental achievement recognizes our innovation in Drug Discovery, specifically powered by our proprietary Chemistry AI Platform and Flow technology. This victory is a significant endorsement of our capabilities, adding unprecedented credibility to our technology's potential to revolutionize not just pharma, but also the future of beauty and personal care.
Sravathi Ai has been selected to the regional finals of the L'Oréal Big Bang India Competition.
It's an exciting time for Sravathi Ai! We're thrilled to announce our selection as Regional Finalists in the prestigious L'Oréal Big Bang India Competition. This recognition is a testament to our team's dedication to innovation and our commitment to pushing boundaries. We look forward to showcasing our progress and competing at the regional level.
CSIR-CDRI collaborates with Bengaluru based startup to develop novel therapeutics for cancer treatment
Leveraging Sravathi’s AI-driven drug-discovery platform and CSIR-CDRI’s extensive cancer R&D infrastructure, this collaboration aims to rapidly design, synthesize, and biologically evaluate novel anti-cancer chemical entities. The joint effort promises speed, cost-efficiency, and a powerful strategic push toward treatments for cancers with limited therapeutic options
Old drugs, new uses
In a May 2023, IIT Madras - Shaastra feature, Sravathi AI’s Kishan Gurram underscores that around 9,000–10,000 molecules already known to be safe in humans offer a powerful avenue for drug repurposing. By leveraging AI and ML to screen and prioritize candidates, Sravathi aims to accelerate identification of high-potential therapies for new indications, vastly reducing development time and cost