Much smarter and faster ligand discovery: Iterative, rational optimization of screening and follow-up libraries for the XChem fragment approach

更智能和更快的配基发现：XChem片段方法的筛选和后续文库的迭代、合理优化和从3D结构数据的深度学习基于碎片的药物设计(FBDD)现在作为一种强大的早期药物发现方法得到了广泛的认可，并在其他方法失败的困难靶点上取得了成功的记录。这是因为与药物大小的配体相比，片段(约三分之一大小的药物)与靶标结合的可能性更高，相互作用更有效。这些弱结合片段然后被连接在一起形成一种有效的药物。基于碎片的配基开发方法，尽管在有经验的组织中得到了很好的确立和相对有效的开发，但尚未实现其真正的变革潜力，即使定制和有效的配基能够以廉价(&lt；GB 10k)和快速(数周)的价格广泛获得。即使是最近旨在扩大准入的重大公共和商业投资，包括戴蒙德的XChem设施或Enamine真正廉价的化合物，也没有从根本上改变游戏规则：开发一种有效的配体仍需花费约50万英镑。这个项目的重点是在实验的一开始就变得更聪明：找出如何确保起始化合物尽可能地有可能产生快速进入效力所需的所有信息，而实验工作尽可能少。这个雄心并不新鲜，但新的是大量的XChem数据(已经有150个实验)，以及理解蛋白质-配体相互作用的新的深度学习方法，以及对合成空间的新描述。在整个DPhil项目结束时，我们设想实现：(A)更好的通用筛选化合物集；(B)为任何给定目标选择最佳筛选化合物集的方法，即使以前没有解决任何配体结合结构；(C)选择最好的下一组化合物的算法，以允许从非常小的初始筛选进行有效迭代；以及(D)允许对经过充分研究的蛋白质类别完全绕过筛选步骤。该项目位于统计系和钻石光源研究所，属于EPSRC生物信息学研究领域。

Much smarter and faster ligand discovery: Iterative, rational optimization of screening and follow-up libraries for the XChem fragment approach with deep learning from 3D structural dataFragment-based drug design (FBDD) is now well-established as a powerful approach to early-stage drug discovery, and has a track record of success for difficult targets where other methods have failed. This is due to the higher likelihood that a fragment (~third size of drug) will bind to the target, with more efficient interactions compared to a drug-sized ligand. These weakly binding fragments are then linked together to create a potent drug.Fragment-based approaches to ligand development, though well-established and comparatively powerful in experienced organisations, have yet to achieve their true transformative potential of making bespoke and potent ligands widely accessible cheaply (<£10k) and quickly (weeks). Even very significant recent public and commercial investments aimed at widening access, including Diamond's XChem facility or Enamine's REAL cheaply available compounds, have not fundamentally changed the game: developing a potent ligand still costs ~£0.5m. The focus of this project is to get much smarter at the very outset of the experiment: to figure out how to ensure the starting compounds are as likely as possible to yield all the information necessary to progress rapidly to potency, with as little experimental work as possible. The ambition is not new, but what is new are a vast trove (5 years' worth) of XChem data (already >150 experiments), and new Deep Learning approaches to understanding protein-ligand interactions, with new descriptions of synthetic space also coming into view.By the end of the full DPhil project, we envisage achieving (a) a far better general set of screening compounds; (b) an approach for selecting the optimal set of screening compounds for any given target, even if no ligand-bound structure has been solved previously; (c) algorithms to select the best next set of compounds, to allow effective iterations from very small initial screens; and (d) allow bypassing the screening step entirely for well-studied classes of protein. This project is based equally in the Department of Statistics and at Diamond Light Source, and falls within the EPSRC Biological Informatics research area.