Molecular docking is often used in computational chemistry to accelerate drug discovery at early stages. Many molecular simulations are performed to select the right pharmacological candidate. However, traditional docking methods are based on optimization heuristics such as Monte Carlo or genetic that try several hundreds of these candidates giving rise to expensive computations. Thus, an alternative methodology called QN-Docking is proposed for developing docking simulations more efficiently. This new approach is built upon Q-learning using a single-layer feedforward neural network to train a single ligand or drug candidate (the agent) to find its optimal interaction with the host molecule. In addition, the corresponding Reinforcement Learning environment and the reward function based on a force-field scoring function are implemented. The proposed method is evaluated in an exemplary molecular scenario based on the kaempferol and beta-cyclodextrin. Results for the prediction phase show that QN-Docking achieves 8$\times$ speedup compared to stochastic methods such as METADOCK 2, a novel high-throughput parallel metaheuristic software for docking. Moreover, these results could be extended to many other ligand-host pairs to ultimately develop a general and faster docking method.

分子对接通常用于计算化学中，以加快早期的药物发现。进行了许多分子模拟以选择合适的药理学候选物。然而，传统的对接方法是基于诸如蒙特卡洛或遗传算法之类的优化试探法，它们尝试了数百种候选方法，从而产生了昂贵的计算量。因此，提出了一种称为QN-Docking的替代方法，以更有效地开发对接仿真。这种新方法是基于Q学习使用单层前馈神经网络来训练单个配体或候选药物（药剂）以发现其与宿主分子的最佳相互作用而建立的。另外，实现了相应的强化学习环境和基于力场评分功能的奖励功能。在基于山molecular酚和β-环糊精的示例性分子方案中评估了所提出的方法。预测阶段的结果表明，QN-对接达到8$\times$与诸如METADOCK 2的随机方法相比，这种方法可以提高速度，METADOCK 2是一种用于对接的新型高通量并行元启发式软件。而且，这些结果可以扩展到许多其他配体-宿主对，以最终开发出一种通用且更快的对接方法。