Where Is the Future of Innovative Drugs? As AI Moves Deeper In, the Industry Stands at a Critical Crossroads
I. A Question Worth Asking
Where is the future of innovative drugs?
This question feels particularly real in the summer of 2026. Last week, we met with a client working in AI-powered healthcare services. He came with a specific request for custom synthesis—their AI platform had identified promising targets, but they needed real compounds for validation, and none were available in existing commercial catalogs.
This client's visit made one question more concrete: as AI moves deeper into drug discovery and healthcare services, what is actually changing across the industry? And how do these changes ripple down to custom synthesis providers like us?
II. A Shift Underway: From Concept Validation to Clinical Validation
2026 is widely regarded as the "year of clinical validation" for AI-driven drug discovery-7. As of the first half of 2026, over 170 drug candidates designed or optimized by AI had entered clinical trials globally, with more than a dozen advancing to Phase III-2-14. AI-driven clinical pipelines grew from 4 in 2017 to 179 by June 2026-1—this is not incremental improvement, but exponential growth-1.
In July, Insilico Medicine announced that its lead pipeline Rentosertib (ISM001-055) had officially initiated a Phase III clinical trial-1. The drug's target discovery, molecular design, and candidate selection were all completed by a generative AI platform-1—making it a milestone for end-to-end AI-driven drug development-1.
What's even more remarkable is the R&D efficiency. From AI-powered target discovery to preclinical candidate nomination, Insilico's Pharma.AI platform took just 18 months, synthesizing and testing fewer than 80 molecules-1. Traditional early-stage drug discovery, by contrast, takes an average of 4.5 years and involves screening thousands of compounds-1. AI is bringing "precision-guided" engineering certainty to drug discovery-1.
III. Commercialization Is Also Accelerating
As the technology matures, commercial value is being realized.
In the first half of 2026, Insilico Medicine secured strategic partnerships with Servier ($888 million), Eli Lilly ($2.75 billion), SK Biopharmaceuticals ($2.5 billion), and Takeda ($600 million)—four deals with a combined potential value approaching **$7 billion**--7. The company expects H1 2026 revenue of $102.5 million to $106.5 million, representing year-over-year growth of approximately 273% to 287%, with projected profitability-2-23.
Alphabet's Isomorphic Labs completed a $2.1 billion Series B financing round, the largest single financing round in the global AI drug discovery industry-. All of the world's top 20 pharmaceutical companies—including Pfizer, Johnson & Johnson, Sanofi, and Novartis—have established deep partnerships with AI drug discovery firms-14.
The industry is shifting from "can it be done" to "can it make money"-2. The next phase of competition in AI is not just about consuming computing power—it's about whether it can translate into real industrial growth-2-23.
IV. From Algorithms to the Lab Bench: A Real Need Is Emerging
But the new targets and molecules identified by AI ultimately need to return to the lab—they require real compounds for validation.
This is exactly the challenge our client last week was facing. Their AI platform could efficiently predict potential targets and candidate molecules, but when it came time to turn those predictions into testable compounds, they couldn't find suitable options in commercial catalogs. What they needed wasn't standardized catalog products, but a series of structural analogs around specific targets—to validate AI predictions, optimize molecular activity, and explore SAR.
AI can generate 100 new targets in a week, but each target's functional validation, pathway dissection, and phenotypic confirmation require high-quality tool compounds and structural analogs. These compounds are often unavailable in any commercial library and must be addressed through custom synthesis. As Wu Xiaoying, Consulting Services Managing Partner at EY Greater China, noted, multinational pharmaceutical companies are willing to pay high premiums "typically because they see an asset that can address a clear clinical problem. AI improves the efficiency of discovery and optimization, but deals are rarely made solely because of AI"-7.
V. Custom Synthesis: The Critical Bridge Between Computation and the Lab Bench
As a custom synthesis service provider based in Shanghai, specializing in inhibitor small molecules, heterocyclic intermediates, and chiral building blocks, Beixinke Chem is witnessing the real-world transmission of this trend.
From target validation to lead optimization, from milligram-scale screening to gram-scale in vivo studies—each stage places different demands on custom synthesis. AI makes it faster to "find molecules that might work," but "turning molecules into testable compounds" still requires chemistry-5.
Our core capabilities cover the entire chain from retrosynthetic analysis to process scale-up: chiral construction, heterocyclic assembly, milligram-to-kilogram scale-up, and full HPLC/LC-MS/NMR analytical support. We have provided custom synthesis support to R&D labs and biotech companies both in China and internationally—across different scales and requirements.
If your R&D team is also using AI for drug discovery and looking for a custom synthesis partner that can turn AI predictions into testable compounds, we would welcome the conversation.