AIMC Topic: Antineoplastic Agents

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Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

Cell communication and signaling : CCS
BACKGROUND: Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung cancer (NSCLC) patients, leading to drug resistance and disease progression. Identification of new selec...

Ultradense Electrochemical Chip and Machine Learning for High-Throughput, Accurate Anticancer Drug Screening.

ACS sensors
Despite the potentialities of electrochemical sensors, these devices still encounter challenges in devising high-throughput and accurate drug susceptibility testing. The lack of platforms for providing these analyses over the preclinical trials of dr...

Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma.

IUBMB life
Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), highlighting the need for alternative therapies. This study explores the genetic basis of GR to improve prediction, prevention, and treatment strategie...

CMINNs: Compartment model informed neural networks - Unlocking drug dynamics.

Computers in biology and medicine
In the field of pharmacokinetics and pharmacodynamics (PKPD) modeling, which plays a pivotal role in the drug development process, traditional models frequently encounter difficulties in fully encapsulating the complexities of drug absorption, distri...

A deep learning model based on the BERT pre-trained model to predict the antiproliferative activity of anti-cancer chemical compounds.

SAR and QSAR in environmental research
Identifying new compounds with minimal side effects to enhance patients' quality of life is the ultimate goal of drug discovery. Due to the expensive and time-consuming nature of experimental investigations and the scarcity of data in traditional QSA...

Triple-Action Therapy: Combining Machine Learning, Docking, and Dynamics to Combat BRCA1-Mutated Breast Cancer.

Molecular biotechnology
Breast cancer dominates women's mortality, and among other factors, mutations in the BRCA1 gene are significant risk factors. Several approaches are followed to treat the BRCA1 affected cancer patients. However, specific BRCA1 inhibitors are not avai...

Targeted isolation and AI-based analysis of edible fungal polysaccharides: Emphasizing tumor immunological mechanisms and future prospects as mycomedicines.

International journal of biological macromolecules
Edible fungal polysaccharides have emerged as significant bioactive compounds with diverse therapeutic potentials, including notable anti-tumor effects. Derived from various fungal sources, these polysaccharides exhibit complex biological activities ...

ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning.

Nature communications
Despite the significant potential of generative models, low synthesizability of many generated molecules limits their real-world applications. In response to this issue, we develop ClickGen, a deep learning model that utilizes modular reactions like ...

Targeting mitochondria in Cancer therapy: Machine learning analysis of hyaluronic acid-based drug delivery systems.

International journal of biological macromolecules
BACKGROUND: Mitochondrial alterations play a crucial role in the development and progression of cancer. Dysfunctional mitochondria contribute to the acquisition of key hallmarks of cancer, including sustained proliferative signaling, evasion of growt...

Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach.

JMIR medical informatics
BACKGROUND: Prostate cancer is the second leading cause of death among American men. If detected and treated at an early stage, prostate cancer is often curable. However, an advanced stage such as metastatic castration-resistant prostate cancer (mCRP...