Background: Although a great number of the targets of antimicrobial therapy have been achieved, it remains among the first fields of pharmaceutical research, mainly because of the development of resistant strains. Docking analysis may be an important tool in the research for the development of more effective agents against specific drug targets or multi-target agents 1-3.

Methods: In the present study, based on docking analysis, ten tetrahydrothiazolo[2,3-a]isoindole derivatives were chosen for the evaluation of the antimicrobial activity.

Results: All compounds showed antibacterial activity against eight Gram-positive and Gram-negative bacterial species being, in some cases, more potent than ampicillin and streptomycin against all species. The most sensitive bacteria appeared to be S. aureus and En. Cloacae, while M. flavus, E. coli and P. aeruginosa were the most resistant ones. The compounds were also tested for their antifungal activity against eight fungal species. All compounds exhibited good antifungal activity better than reference drugs bifonazole (1.4 – 41 folds) and ketoconazole (1.1 – 406 folds) against all fungal species. In order to elucidate the mechanism of action, docking studies on different antimicrobial targets were performed.

Conclusion: According to docking analysis, the antifungal activity can be explained by the inhibition of the CYP51 enzyme for most compounds with a better correlation of the results obtained for the P.v.c. strain (linear regression between estimated binding Energy and log(1/MIC) with R 2 =0.867 and p=0.000091 or R 2 = 0.924, p= 0.000036, when compound 3 is excluded.

背景：尽管已经实现了许多抗微生物治疗的目标，但由于抗药性菌株的发展，它仍然是药物研究的第一个领域。对接分析可能是研究中针对特定药物靶标或多靶标药物1-3的更有效试剂开发的重要工具。

方法：在本研究中，基于对接分析，选择了十种四氢噻唑并[2,3-a]异吲哚衍生物来评估其抗菌活性。

结果：所有化合物对8种革兰氏阳性和革兰氏阴性细菌均表现出抗菌活性，在某些情况下，对所有物种的效力均比氨苄西林和链霉素强。最敏感的细菌似乎是金黄色葡萄球菌和En。泄殖腔，而黄曲霉，大肠杆菌和铜绿假单胞菌是最有抵抗力的。还测试了这些化合物对八种真菌的抗真菌活性。所有化合物对所有真菌均表现出比参比药物联苯苄唑（1.4-41倍）和酮康唑（1.1-406倍）更好的良好的抗真菌活性。为了阐明作用机理，对不同的抗菌目标进行了对接研究。

结论：根据对接分析，可以通过抑制大多数化合物的CYP51酶来解释其抗真菌活性，并与Pvc菌株获得的结果具有更好的相关性（估计的结合能与log（1 / MIC）之间的线性回归与当排除化合物3时，R 2 ＝ 0.867且p ＝ 0.000091或R 2 ＝ 0.924，p ＝ 0.000036。