A.I. Team Leader
The Message of Muhammed Yanık
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The new A.I. that can design drug derivatives has the capability to integrate itself to a suite of chemistry and simulation libraries to provide robust chemoinformatics reasoning and high-quality molecular visualization. By leveraging this open-source ecosystem, we not only minimize the costs of commercial drug discovery software but also maintain flexibility, transparency, and license compliance. The framework is deliberately modular, enabling extension, customization, and optimization for evolving research goals.
Progress so far:
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Built and validated the AI backend.
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Integrated core chemistry libraries.
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Released an initial version of the graphical UI.
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Ran early simulations with encouraging docking results.
What’s next:
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Implement CUDA-based GPU acceleration to cut response times.
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Finalize large-scale automated docking workflows with AutoDock Vina.
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Deliver interactive molecular visualizations directly on the web platform.
In short, it represents a locally developed, future-ready AI environment for drug discovery—one that can generate optimized candidates, streamline docking workflows, and support end-to-end validation.
Dear All,
We’re building a next-generation AI platform, , to accelerate the discovery of novel drug candidates. Unlike conventional pipelines, this system is not constrained by rigid workflows. Instead, it dynamically assigns functional groups, runs conformational analyses, and evaluates hundreds of docking poses. From these simulations, it automatically surfaces the top 3–5 optimized candidates, which are then rendered in real time with molecular viewers and exported in standard formats (.pdb, .pdbqt) for downstream validation.
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