Simultaneous use of the drugs may lead to undesirable Drug-Drug Interactions (DDIs) in the human body. Many DDIs are associated with changes in drug metabolism that performed by Drug-Metabolizing Enzymes (DMEs). In this case, DDI manifests itself as a result of the effect of one drug on the biotransformation of other drug(s), its slowing down (in the case of inhibiting DME) or acceleration (in case of induction of DME), which leads to a change in the pharmacological effect of the drugs combination. We used OpeRational ClassificAtion (ORCA) system for categorizing DDIs. ORCA divides DDIs into five classes: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). We collected a training set consisting of several thousands of drug pairs. Algorithm of PASS program was used for the first, second and third classes DDI prediction. Chemical descriptors called PoSMNA (Pairs of Substances Multilevel Neighbourhoods of Atoms) were developed and implemented in PASS software to describe in a machine-readable format drug substances pairs instead of the single molecules. The average accuracy of DDI class prediction is about 0.84. A freely available web resource for DDI prediction was developed (http://way2drug.com/ddi/).

同时使用药物可能会导致人体发生不良的药物相互作用（DDI）。许多DDI与药物代谢酶（DME）引起的药物代谢变化有关。在这种情况下，DDI表现为一种药物对其他药物的生物转化，减慢速度（在抑制DME的情况下）或加速（在诱导DME的情况下）的结果，从而导致改变药物组合的药理作用。我们使用操作分类（ORCA）系统对DDI进行分类。ORCA将DDI分为五类：禁忌（1类），暂时禁忌（2类），有条件（3类），最小风险（4类），无交互作用（5类）。我们收集了包含数千对药物的训练集。PASS程序的算法用于第一，第二和第三类DDI预测。已开发并实施了称为PoSMNA（原子对物质的多级邻域对）的化学描述符，并在PASS软件中实施了该描述符，以机器可读格式描述了成对的药物而不是单个分子。DDI类预测的平均准确度约为0.84。开发了免费的DDI预测Web资源（http://way2drug.com/ddi/）。