Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug–ADR associations often entails expensive and time-consuming investigations. Thus, a computational model to predict ADRs from known associations is essential for enhanced efficiency and cost-effectiveness. Here, we propose BiMPADR, a novel model that integrates drug gene expression into adverse reaction features using a message passing neural network on a bipartite graph of drugs and adverse reactions, leveraging publicly available data. By combining the computed adverse reaction features with the structural fingerprints of drugs, we predict the association between drugs and adverse reactions. Our models obtained high AUC (area under the receiver operating characteristic curve) values ranging from 0.861 to 0.907 in an external drug validation dataset under differential experiment conditions. The case study on multiple BET inhibitors also demonstrated the high accuracy of our predictions, and our model’s exploration of potential adverse reactions for HWD-870 has contributed to its research and development for market approval. In summary, our method would provide a promising tool for ADR prediction and drug safety assessment in drug discovery and development.

Background: There have been significant advancements in melanoma therapies. BET inhibitors (BETis) show promise in impairing melanoma growth. However, identifying BETi-sensitive melanoma subtypes is challenging. Methods and Results: We analyzed 48 melanoma cell lines and 104 patients and identified two acetylation-immune subtypes (ALISs) in the cell lines and three ALISs in the patients. ALIS I, with high HAT1 and low KAT2A expression, showed a higher sensitivity to the BETi JQ-1 than ALIS II. ALIS III had low HAT1 expression. The TAD2B expression was low in ALIS I and II. KAT2A and HAT1 expressions were negatively correlated with the methylation levels of their CG sites (p = 0.0004 and 0.0003). Immunological gene sets, including B cell metagenes, activated stroma-related genes, fibroblast TGF response signatures (TBRS), and T cell TBRS-related genes, were up-regulated in ALIS I. Furthermore, KAT2A played a key role in regulating BETi sensitivity. Conclusions: The sensitivity of ALIS I to the BETi JQ-1 may be due to the inhibition of BETi resistance pathways and genes by low KAT2A expression and the dysregulation of the immune microenvironment by high HAT1 expression resulting from the absence of immune cells. ALIS I had the worst progression but showed sensitivity to BETi and B-cell-related immunotherapy, despite not responding to BRAF inhibitors.