Prolonged antibiotic therapy for the bacterial infections has resulted in high levels of antibiotic resistance. Initially, bacteria are susceptible to the antibiotics, but can gradually develop resistance. Treating such drug-resistant bacteria remains difficult or even impossible. Hence, there is a need to develop effective drugs against bacterial pathogens. The drug discovery process is time-consuming, expensive and laborious. The traditionally available drug discovery process initiates with the identification of target as well as the most promising drug molecule, followed by the optimization of this, in-vitro, in-vivo and in pre-clinical studies to decide whether the compound has the potential to be developed as a drug molecule. Drug discovery, drug development and commercialization are complicated processes. To overcome some of these problems, there are many computational tools available for new drug discovery, which could be cost effective and less time-consuming. In-silico approaches can reduce the number of potential compounds from hundreds of thousands to the tens of thousands which could be studied for drug discovery and this results in savings of time, money and human resources. Our review is on the various computational methods employed in new drug discovery processes.

对于细菌感染的长期抗生素治疗已导致高水平的抗生素抗性。最初，细菌对抗生素敏感，但可以逐渐产生抗药性。治疗这种抗药性细菌仍然困难，甚至不可能。因此，需要开发针对细菌病原体的有效药物。药物发现过程耗时，昂贵且费力。传统上可用的药物发现过程始于对靶标以及最有希望的药物分子的鉴定，然后对其进行体外，体内优化并在临床前研究中确定该化合物是否具有被开发为药物分子的潜力。药物发现，药物开发和商业化是复杂的过程。为了克服这些问题中的一些，有许多可用于新药发现的计算工具，它们可能具有成本效益，并且耗时较少。硅内方法可以将潜在化合物的数量从数十万减少到数以万计，可以将其研究用于药物发现，从而节省时间，金钱和人力资源。我们的综述是关于新药发现过程中采用的各种计算方法。