Manas AI & Schrödinger: A New Era for AI-Driven Drug Discovery

NEW YORK, NY – January 12, 2026 – In a move poised to reshape the landscape of pharmaceutical research, AI-native drug discovery firm Manas AI has announced a landmark strategic agreement with Schrödinger, the well-established leader in physics-based computational software. The multi-year deal provides Manas AI with unprecedented deep access to Schrödinger’s discovery platform, creating a powerful synergy between artificial intelligence and the fundamental laws of molecular physics.

This partnership is more than a simple technology license; it represents a deep integration aimed at creating a next-generation drug discovery engine. Manas AI, which is building what it calls an atomic "world model," intends to leverage Schrödinger’s platform at an ultra-large scale to accelerate the identification of novel binders—including small molecules, nanobodies, and siRNAs—for disease-causing proteins. The collaboration signals a pivotal moment in the industry's quest to make drug development faster, cheaper, and more predictable.

The Minds Behind the Mission

The ambition of Manas AI is underpinned by its trio of high-profile co-founders: Dr. Siddhartha Mukherjee, Reid Hoffman, and Ujjwal Singh. This team combines deep scientific acumen, legendary tech entrepreneurship, and substantial financial backing, positioning the company as a formidable new player.

Dr. Mukherjee, who serves as CEO, is a practicing oncologist, a Pulitzer Prize-winning author for "The Emperor of All Maladies: A Biography of Cancer," and a seasoned life science entrepreneur. His track record includes co-founding successful biotech companies like Vor Biopharma and Faeth Therapeutics. His direct experience with the inefficiencies of traditional drug development fuels Manas AI's mission. "Our mission at Manas AI is to solve the most complex challenges in drug discovery and development by merging the best of AI with the most rigorous physics," Mukherjee stated in the announcement.

Alongside him is Reid Hoffman, a titan of Silicon Valley best known for co-founding LinkedIn and his early investments in giants like Facebook and Airbnb. As a partner at Greylock and co-founder of AI-pioneer Inflection AI, Hoffman brings unparalleled expertise in scaling technology platforms and a profound belief in AI's transformative potential. His involvement underscores the venture's serious technological and commercial goals.

The company's technical vision is steered by Co-founder and CTO Ujjwal Singh. With a total of $50.6 million in seed funding from rounds co-led by General Catalyst and Hoffman himself, Manas AI has the capital to pursue its ambitious goals, including scaling its proprietary AI platform and advancing its pipeline of drug candidates.

Forging the AI-Physics Nexus

The core of the new agreement lies in the fusion of two distinct but complementary technologies. Manas AI is developing neuro-symbolic, science-based foundational models designed to operate as an "AI-enabled oracle" for molecular discovery. Schrödinger, conversely, is the gold standard for physics-based computational chemistry. Its platform uses principles of quantum mechanics and free energy perturbation (FEP+) to simulate and predict how molecules will interact with high accuracy.

The term "deep integration" in the agreement is significant. It grants Manas AI not just software access, but also priority support from Schrödinger’s scientific and technical teams. This bespoke arrangement is designed to harmonize Manas AI’s machine learning algorithms with Schrödinger’s predictive power, creating a feedback loop where AI-generated hypotheses are rigorously tested by physics-based models, and the results of those simulations, in turn, refine the AI.

"This isn't just an addition to our toolkit; it is a foundational pillar of our discovery engine that we believe gives us a distinct competitive advantage," Mukherjee explained. The ability to run these simulations at an "ultra-large scale"—powered by its existing cloud computing partnership with Microsoft Azure—is critical. It allows the company to explore a vast chemical space far more rapidly than traditional methods would permit.

Schrödinger's endorsement of Manas AI's approach is a powerful validation. "The next frontier in drug discovery is the seamless convergence of physics and AI, and we are very impressed by the sophisticated approach Manas AI is taking," said Pat Lorton, Chief Technology Officer and Chief Operating Officer of Software at Schrödinger. "Because of the scale and ambition of their vision, we have committed to a deeper level of support and platform access."

A Strategic Play in a Competitive Arena

Manas AI enters a vibrant and increasingly crowded field. Companies like Recursion Pharmaceuticals, Insilico Medicine, and Exscientia have already made significant strides in using AI to accelerate drug discovery. Recursion boasts major partnerships with Roche and Bayer, while Insilico was the first to advance a drug designed entirely by generative AI into human clinical trials. UK-based Exscientia has demonstrated its ability to deliver drug candidates in 12 to 15 months, a fraction of the typical five-year timeline.

In this context, the Schrödinger partnership is a calculated strategic maneuver. Rather than building its physics-based simulation capabilities from scratch, Manas AI is effectively leasing the industry's best-in-class engine and turbocharging it with its proprietary AI. This allows the company to focus its resources on developing its unique "world model" while leveraging decades of Schrödinger's specialized development.

This hybrid approach could offer a crucial edge. While many AI platforms excel at identifying patterns in vast datasets, their predictive accuracy can be a limitation. By grounding its AI in the proven, first-principles accuracy of Schrödinger's physics engine, Manas AI aims to improve its predictive power, potentially leading to higher-quality drug candidates and a lower failure rate in later-stage development.

Redefining the Drug Development Timeline

The ultimate prize for Manas AI and its competitors is to fundamentally alter the economics of drug development. The traditional path from lab to pharmacy takes 10 to 15 years and can cost upwards of $2 billion, with a staggeringly high rate of failure. The promise of AI is to slash both the time and the cost.

Industry data suggests this promise is becoming a reality. Some reports indicate generative AI could reduce early-discovery timelines by as much as 70%, while AI-discovered drugs that enter clinical trials appear to have a higher success rate than their traditionally developed counterparts. Companies are already showing tangible results: Insilico Medicine advanced a preclinical candidate for a fibrotic disease in just 18 months for a cost of $2.6 million, a process that would normally take years and far more capital.

Manas AI’s claim that this partnership will "significantly shorten our discovery timelines" aligns with these industry-wide ambitions. By combining massive-scale computation with highly accurate physical modeling, the company hopes to rapidly screen billions of potential molecules, identify promising candidates, and optimize them for efficacy and safety in silico, long before they are ever synthesized in a lab.

This dedication to a fully "AI-native" workflow, now fortified by a world-class physics engine, represents one of the most ambitious bets yet on the power of computation to solve biology's most complex puzzles. As Manas AI integrates Schrödinger's physical precision with its artificial intelligence, the entire pharmaceutical industry will be watching to see if this high-stakes fusion can truly deliver on its promise to build a faster, more efficient path to tomorrow's cures.