AIMC Topic: Drug Discovery

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A new era of antibody discovery: an in-depth review of AI-driven approaches.

Drug discovery today
Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consu...

Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning.

Nature chemical biology
Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been ...

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling.

Computers in biology and medicine
Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunoth...

PT-Finder: A multi-modal neural network approach to target identification.

Computers in biology and medicine
Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive,...

DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.

Methods (San Diego, Calif.)
Epigenetic proteins (EP) play a role in the progression of a wide range of diseases, including autoimmune disorders, neurological disorders, and cancer. Recognizing their different functions has prompted researchers to investigate them as potential t...

BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs.

Molecules (Basel, Switzerland)
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 t...

Emerging structure-based computational methods to screen the exploding accessible chemical space.

Current opinion in structural biology
Structure-based virtual screening can be a valuable approach to computationally select hit candidates based on their predicted interaction with a protein of interest. The recent explosion in the size of chemical libraries increases the chances of hit...

Prediction of drug-target binding affinity based on deep learning models.

Computers in biology and medicine
The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, thes...

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis.

International journal of molecular sciences
Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have fail...