Efficient calculation of binding free-energies and druggability in challenging drug targets

最近的估计显示，一种新药的开发成本超过20亿美元。1在进入药物发现流程的大量化合物中，只有一小部分进入市场。后期(临床)阶段的失败代价极其高昂。因此，及早识别与生物验证靶标相互作用的先导化合物，并在不引起不良反应(毒性)的情况下发挥其作用机制，是非常重要的。在这方面，预计计算方法将发挥越来越大的作用。快速对接算法现在可以可靠地预测结合模式2，但基于亲和力的化合物排名仍然需要更深入地了解缔合机制。Francesco的团队为了解结合机制做出了贡献，并开发了准确计算结合自由能的方法。在这里，我们计划以我们在方法开发和药物发现方面的丰富经验为基础，开发一个计算平台，以达到在廉价图形处理单元(GPU)上进行半定量和定量评分所需的准确性。所提出的技术将由UCB通过生物物理(微量热法，SPR)和结构方法(结晶学)进行实验验证。

Recent estimates put the development cost of a new medicine at over 2 billion dollars.1 Of the enormous number of compounds entering the drug discovery pipeline, only a tiny fraction reach the market. Failures in the later (clinical) stages are extremely costly. It is therefore of great importance to identify early on lead compounds that interact with biologically validated target and exert their mechanism without inducing adverse effects (toxicity). In this respect, computational approaches are expected to play an increasing role. Fast docking algorithms can now reliably predict binding modes.2 But the ranking of compounds based on affinity still requires a more in-depth understanding of the association mechanisms.Francesco's group contributed to understand binding mechanisms and developed methods for accurate binding free energy calculations. Here we plan to build on our extensive experience in method development and drug discovery and develop a computational platform to achieve the required accuracy for semi-quantitative and quantitative scoring on cheap graphic processing units (GPUs). The proposed techniques will be experimentally validated by UCB by biophysical (microcalorimetry, SPR) and structural approaches (crystallography).