Ronit Kumar Choudhary, a 21-year-old third-year student at Newton School of Technology, has co-authored “RETROSPECT: RETROsynthesis via Sequential Prediction, and Chemically Transformed-ranking”, a research paper accepted at the AI for Science Workshop at ICML 2026. The work is also available on arXiv and focuses on applying AI to retrosynthesis, a core problem in chemistry that helps scientists determine how complex molecules can be constructed from simpler building blocks. The achievement is notable because Ronit is still an undergraduate. His journey moved from discovering AI and machine learning through coursework to building applied projects, securing a paid AI/ML internship at Mstack AI, and contributing to a global AI-for-science research forum within a short span of time. His trajectory reflects a broader shift in which Indian engineering students are entering frontier AI research earlier than before.Why this matters ICML, the International Conference on Machine Learning, is one of the world’s leading venues for AI research. Its workshop tracks serve as focused forums where emerging ideas are discussed and refined before broader adoption in the research community. The AI for Science Workshop specifically explores how machine learning can accelerate breakthroughs in fields such as chemistry, biology, physics, materials science, and drug discovery. Ronit’s paper fits directly into this domain. Retrosynthesis is a fundamental challenge in computational chemistry: given a target molecule, researchers aim to determine the sequence of chemical reactions needed to synthesize it. In simple terms, it answers the question—how can a desired molecule actually be made in a lab? Solving this efficiently has major implications for drug development and materials innovation, where identifying viable synthesis routes can be time-consuming and complex.The research RETROSPECT proposes a two-stage approach combining a Transformer-based model for generating candidate synthesis routes with a ranking system that evaluates and prioritises the most promising options. According to the paper, the model demonstrates strong performance on the USPTO-50K benchmark, achieving 55.00% top-1 accuracy, 86.18% top-10 accuracy, and 99.86% validity at top-1. The reranking module further improves top-1 accuracy to 59.4%. In practical terms, the system is designed to help chemists narrow down better possible pathways for synthesizing molecules, improving the speed and reliability of research in computational chemistry and related fields.Ronit’s journey Ronit’s interest in AI and machine learning began during his coursework at Newton School of Technology. As he explored the field further, he began working on independent projects and actively sought opportunities in applied AI. He later joined Mstack AI as a paid AI/ML intern, a company working at the intersection of artificial intelligence and chemistry. While he initially expected to work on product-focused applications, he was introduced to deeper research problems involving AI-driven molecular discovery. During a focused 45-day internship sprint, Ronit contributed to the development of RETROSPECT under the guidance of the research team. The work involved experimentation, iterative model improvements, and discussions around chemical prediction systems—exposing him to real-world AI research workflows at an early stage in his career.Global research context The AI for Science Workshop at ICML operates within a broader research ecosystem that includes contributions from organisations and institutions such as Google DeepMind, Anthropic, Meta FAIR, Microsoft Research AI for Science, Isomorphic Labs, and leading universities including MIT, Stanford, Harvard, Princeton, Cambridge, Caltech, Cornell, and EPFL. Within this context, workshop-level ICML acceptances represent participation in a highly competitive global research environment. While not part of the main conference track, these workshops are peer-reviewed and serve as important platforms for emerging ideas in machine learning research. Ronit’s inclusion in this space reflects the increasing visibility of undergraduate researchers in advanced AI domains, particularly in interdisciplinary fields like AI for science.A growing trend in Indian AI research Ronit Chaodhary’s work highlights a broader trend in India’s AI ecosystem: students are increasingly engaging with frontier research during their undergraduate years, supported by startups, internships, and open research collaboration. His progression—from classroom learning to applied internships and then to a globally recognised research workshop—illustrates how early exposure to real-world AI systems is reshaping traditional academic pathways in machine learning and scientific research.Ready to navigate global policies? Secure your overseas future. Get expert guidance now!