AIMC Topic: Antineoplastic Agents
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Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer.
Current drug delivery
Jan 1, 2024
Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intel...
Predicting Antitumor Activity of Anthrapyrazole Derivatives using Advanced Machine Learning Techniques.
Current computer-aided drug design
Jan 1, 2024
BACKGROUND: Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors.
CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.
Briefings in bioinformatics
Jan 19, 2023
Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug sy...
canSAR: update to the cancer translational research and drug discovery knowledgebase.
Nucleic acids research
Jan 6, 2023
canSAR (https://cansar.ai) is the largest public cancer drug discovery and translational research knowledgebase. Now hosted in its new home at MD Anderson Cancer Center, canSAR integrates billions of experimental measurements from across molecular pr...
Recent Trends in Computer-aided Drug Design for Anti-cancer Drug Discovery.
Current topics in medicinal chemistry
Jan 1, 2023
Cancer is considered one of the deadliest diseases globally, and continuous research is being carried out to find novel potential therapies for myriad cancer types that affect the human body. Researchers are hunting for innovative remedies to minimiz...
Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.
Briefings in bioinformatics
Nov 5, 2021
Drug combinations have exhibited promising therapeutic effects in treating cancer patients with less toxicity and adverse side effects. However, it is infeasible to experimentally screen the enormous search space of all possible drug combinations. Th...
DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.
Briefings in bioinformatics
Sep 2, 2021
Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. Among various obstacles hindering ...
Anticancer peptides prediction with deep representation learning features.
Briefings in bioinformatics
Sep 2, 2021
Anticancer peptides constitute one of the most promising therapeutic agents for combating common human cancers. Using wet experiments to verify whether a peptide displays anticancer characteristics is time-consuming and costly. Hence, in this study, ...
Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network.
Briefings in bioinformatics
Sep 2, 2021
Triple-negative breast cancer (TNBC) has been a challenging breast cancer subtype for oncological therapy. Normally, it can be classified into different molecular subtypes. Accurate and stable classification of the six subtypes is essential for perso...
Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.
Briefings in bioinformatics
Jul 20, 2021
Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique ability to target cancer cells without affecting healthy cells directly, they have been extensively studied. Many peptide-based drugs are currently evalua...
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