AIMC Topic: Antineoplastic Agents
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Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.
Lab on a chip
Jun 11, 2024
Despite recent advances in cancer treatment, refining therapeutic agents remains a critical task for oncologists. Precise evaluation of drug effectiveness necessitates the use of 3D cell culture instead of traditional 2D monolayers. Microfluidic plat...
Discovery of a Novel and Potent LCK Inhibitor for Leukemia Treatment via Deep Learning and Molecular Docking.
Journal of chemical information and modeling
Jun 7, 2024
The lymphocyte-specific protein tyrosine kinase (LCK) plays a crucial role in both T-cell development and activation. Dysregulation of LCK signaling has been demonstrated to drive the oncogenesis of T-cell acute lymphoblastic leukemia (T-ALL), thus p...
Application of molecular dynamics-based pharmacophore and machine learning approaches to identify novel Mcl1 inhibitors through drug repurposing and mechanics research.
Physical chemistry chemical physics : PCCP
Jun 6, 2024
Myeloid cell leukemia 1 (Mcl1), a critical protein that regulates apoptosis, has been considered as a promising target for antitumor drugs. The conventional pharmacophore screening approach has limitations in conformation sampling and data mining. He...
TumFlow: An AI Model for Predicting New Anticancer Molecules.
International journal of molecular sciences
Jun 4, 2024
Melanoma is the fifth most common cancer in the United States. Conventional drug discovery methods are inherently time-consuming and costly, which imposes significant limitations. However, the advent of Artificial Intelligence (AI) has opened up new ...
Reliable anti-cancer drug sensitivity prediction and prioritization.
Scientific reports
May 29, 2024
The application of machine learning (ML) to solve real-world problems does not only bear great potential but also high risk. One fundamental challenge in risk mitigation is to ensure the reliability of the ML predictions, i.e., the model error should...
OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data.
Cellular oncology (Dordrecht, Netherlands)
May 28, 2024
PURPOSE: Pancreatic Ductal Adenocarcinoma (PDAC) remains a challenging disease due to its complex biology and aggressive behavior with an urgent need for efficient therapeutic strategies. To assess therapy response, pre-clinical PDAC organoid-based m...
Catalyzing innovation in cancer drug discovery through artificial intelligence, machine learning and patency.
Pharmaceutical patent analyst
May 21, 2024
A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research.
Nature nanotechnology
May 15, 2024
Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a ...
Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.
Journal of chemical information and modeling
May 13, 2024
Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale dr...
Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning.
BMC bioinformatics
May 9, 2024
BACKGROUND: The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretabili...
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