The problem with trial-and-error approach in organic synthesis of targeted anticancer compounds can be successfully avoided by computational modeling of molecules, docking studies and chemometric tools. It has been proven that A- and B- modified d-homo lactone and d-seco androstane derivatives are compounds with significant antiproliferative activity against estrogen-independent breast adenocarcinoma (ER-, MDA-MB-231) and androgen-independent prostate cancer cells (AR-, PC-3). This paper presents the quantitative structure-activity relationship (QSAR) models based on artificial neural networks (ANNs) which are able to predict whether d-homo lactone and/or d-seco androstane-based compounds will express antiproliferative activity against breast cancer cells (MDA-MB-231) or not. Also, the present paper describes the molecular docking study of 3β-acetoxy-5α,6α-epoxy- (3) and 6α,7α-epoxy-1,4-dien-3-one (24) d-homo lactone androstane derivatives, as well as 4-en-3-one (15) d-seco androstane derivative, which are compounds with strong or moderate antiproliferative activity against prostate cancer cells (PC-3), and compares them with commercially available medicament for prostate cancer – abiraterone. The obtained promising results can be used as guidelines in further syntheses of novel d-homo lactone and d-seco androstane derivatives with antiproliferative activity against breast and prostate cancer cells.