AIMC Topic: Drug Discovery
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FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology.
Journal of chemical information and modeling
Apr 23, 2024
In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machin...
Artificial Intelligence-Assisted Optimization of Antipigmentation Tyrosinase Inhibitors: Molecular Generation Based on a Low Activity Lead Compound.
Journal of medicinal chemistry
Apr 23, 2024
Artificial intelligence (AI) molecular generation is a highly promising strategy in the drug discovery, with deep reinforcement learning (RL) models emerging as powerful tools. This study introduces a fragment-by-fragment growth RL forward molecular...
[The revolution of AI in drug development].
Medecine sciences : M/S
Apr 23, 2024
Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to repre...
A new era of antibody discovery: an in-depth review of AI-driven approaches.
Drug discovery today
Apr 18, 2024
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
Apr 17, 2024
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
Apr 16, 2024
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
Apr 15, 2024
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.)
Apr 14, 2024
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)
Apr 14, 2024
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
Apr 10, 2024
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...
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