You bet that ML & AI will advance chemical synthesis like anything before!
"A new machine learning tool for drug design proposes better and more realistic drugs through clever handling of molecular geometries. The FragGen software builds molecules fragment by fragment, and uses different machine learning processes for each decision, to minimise the inherent drawbacks of each. FragGen’s creators were able to select an anticancer target, design a new drug, synthesise it and demonstrate its potency experimentally. ..."
From the absract:
"3D structure-based molecular generation is a successful application of generative AI in drug discovery. Most earlier models follow an atom-wise paradigm, generating molecules with good docking scores but poor molecular properties (like synthesizability and drugability). In contrast, fragment-wise generation offers a promising alternative by assembling chemically viable fragments. However, the co-design of plausible chemical and geometrical structures is still challenging, as evidenced by existing models. To address this, we introduce the Deep Geometry Handling protocol, which decomposes the entire geometry into multiple sets of geometric variables, looking beyond model architecture design. Drawing from a newly defined six-category taxonomy, we propose FragGen, a novel hybrid strategy as the first geometry-reliable, fragment-wise molecular generation method. FragGen significantly enhances both the geometric quality and synthesizability of the generated molecules, overcoming major limitations of previous models. Moreover, FragGen has been successfully applied in real-world scenarios, notably in designing type II kinase inhibitors at the ∼nM level, establishing it as the first validated 3D fragment-based drug design algorithm. We believe that this concept-algorithm-application cycle will not only inspire researchers working on other geometry-centric tasks to move beyond architecture designs but also provide a solid example of how generative AI can be customized for drug design."
Fig. 2 (A) Illustration of symmetry requirements for various geometric variables. (B) Structure-aware and fragment-wise molecular generation (C). Workflow of our proposed combined geometry handling protocol, which is specifically designed for 3D fragment-wise molecular generation.
Fig. 3 This figure presents a comparative illustration of workflows, challenges, objectives, and implementations across different geometry handling protocols. The ‘example’ column focuses on applications within the field of 3D molecular generation, while the ‘other models' column spans a broader range of geometry-centric topics. Key abbreviations include MG: Molecular Generation (without structures), S-MG: Structure-based Molecular Generation, CG: Conformation Generation (without structures), and S-CG: Structure-based Conformation Generation (also known as Docking).
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