Yann Gaston-Mathé, Iktos Co-founder & CEO, is interviewed by Drug Target Review to discuss how chemistry-aware GenAI is reshaping the landscape of small molecule design.
The article highlights how combining deep chemical expertise with generative AI can accelerate the path from idea to synthesis — bridging the gap between in silico design and experimental reality.
You will find the link to the full article below the excerpt:
Makya’s chemistry-aware design
Makya, Iktos’ flagship platform, was built to address this issue directly. Rather than producing molecules as strings of characters, the system builds them step by step using known reactions and real starting materials.
The key difference is that Makya does not generate molecules as strings that merely resemble known chemistry. It performs what I would call iterative virtual chemistry.
“The key difference is that Makya does not generate molecules as strings that merely resemble known chemistry. It performs what I would call iterative virtual chemistry,” Gaston-Mathé explains.
The neural network selects chemical building blocks before applying reactions in sequence. Users can constrain suppliers, prices or the number of steps, ensuring that synthetic routes are realistic from the outset.
“Because Makya builds molecules via sequences of feasible reactions on real starting materials, synthetic accessibility is guaranteed and controllable,” he says. Equally important, the system was designed for chemists: “It mirrors how chemists think and work, letting them impose precise constraints and express their intuition.”
Benchmarks in practice
Benchmarking results have suggested that Makya outperforms leading open-source approaches such as REINVENT 4. For Gaston-Mathé, the critical test is whether candidates are both feasible and diverse.
For people running real programmes, two things matter above all: can we make the molecules and do they broaden our options rather than repeat the same idea. That is exactly where Makya’s chemistry-aware approach shines.
“For people running real programmes, two things matter above all: can we make the molecules and do they broaden our options rather than repeat the same idea. That is exactly where Makya’s chemistry-aware approach shines.”
Head-to-head comparisons showed Makya producing a larger share of compounds with viable synthetic routes and offering more scaffold diversity. The platform also enforces real-world constraints from the start – one-step couplings, specified reaction sets or limited building block catalogues – rather than filtering after generation.
“In practice, that is the difference between hundreds of lab-ready candidates versus a handful that slip through filters,” he says.
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