Background: IC₅₀ is one of the most important parameters of a drug. But, it is very difficult to predict this value of a new compound without experiment. There are only a few QSAR based methods available for IC₅₀ prediction, which is also highly dependable on a huge number of known data. Thus, there is an immense demand for a sophisticated computational method of IC₅₀ prediction in the field of in silico drug designing.

Objective: Recently developed quantum computation based method of IC₅₀ prediction by Bag and Ghorai requires an affordable known data. In present research work, further development of this method is carried out such that the requisite number of known data being minimal.

Methods: To retrench the cardinal data span and shrink the effects of variant biological parameters on the computed value of IC₅₀, a relative approach of IC₅₀ computation is pursued in the present method. To predict an approximate value of IC₅₀ of a small molecule, only the IC₅₀ of a similar kind of molecule is required for this method.

Results: The present method of IC₅₀ computation is tested for both organic and organometallic compounds as HIV-1 capsid A inhibitor and cancer drugs. Computed results match very well with the experiment.

Conclusion: This method is easily applicable to both organic and organometallic compounds with acceptable accuracy. Since this method requires only the dipole moments of an unknown compound and the reference compound, IC₅₀ based drug search is possible with this method. An algorithm is proposed here for IC₅₀ based drug search.

背景：IC ₅₀是药物最重要的参数之一。但是，没有实验就很难预测新化合物的这个值。只有少数基于 QSAR 的方法可用于 IC ₅₀预测，而且这些方法对大量已知数据也非常可靠。因此，在计算机药物设计领域对IC ₅₀预测的复杂计算方法存在巨大需求。

目标：Bag 和 Ghorai最近开发的基于量子计算的 IC ₅₀预测方法需要负担得起的已知数据。在目前的研究工作中，对该方法进行了进一步的开发，使得所需的已知数据数量最少。

方法：为了缩减基本数据跨度并缩小变异生物参数对IC ₅₀计算值的影响，本方法追求IC ₅₀计算的相对方法。为了预测小分子的IC ₅₀的近似值，该方法只需要类似分子的IC ₅₀。

结果： 对作为 HIV-1 衣壳 A 抑制剂和抗癌药物的有机和有机金属化合物测试了本 IC ₅₀计算方法。计算结果与实验非常吻合。

结论：该方法适用于有机和有机金属化合物，准确度可接受。由于此方法只需要未知化合物和参考化合物的偶极矩，因此可以使用此方法进行基于IC ₅₀的药物搜索。这里提出了一种用于基于IC ₅₀的药物搜索的算法。