AIMC Topic: Drug Discovery
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Enhancing the Predictive Power of Machine Learning Models through a Chemical Space Complementary DEL Screening Strategy.
Journal of medicinal chemistry
Oct 23, 2024
DNA-encoded library (DEL) technology is an effective method for small molecule drug discovery, enabling high-throughput screening against target proteins. While DEL screening produces extensive data, it can reveal complex patterns not easily recogniz...
Graph Curvature Flow-Based Masked Attention.
Journal of chemical information and modeling
Oct 23, 2024
Graph neural networks (GNNs) have revolutionized drug discovery in chemistry and biology, enhancing efficiency and reducing resource demands. However, classical GNNs often struggle to capture long-range dependencies due to challenges like oversmoothi...
Machine Learning-Driven Data Valuation for Optimizing High-Throughput Screening Pipelines.
Journal of chemical information and modeling
Oct 23, 2024
In the rapidly evolving field of drug discovery, high-throughput screening (HTS) is essential for identifying bioactive compounds. This study introduces a novel application of data valuation, a concept for evaluating the importance of data points bas...
Drugs from drugs: New chemical insights into a mature concept.
Drug discovery today
Oct 22, 2024
Developing new drugs from marketed ones is a well-established and successful approach in drug discovery. We offer a unified view of this field, focusing on the new chemical aspects of the involved approaches: (a) chemical transformation of the origin...
Advancing Anticancer Drug Discovery: Leveraging Metabolomics and Machine Learning for Mode of Action Prediction by Pattern Recognition.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Oct 21, 2024
A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate can...
Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases.
Drug discovery today
Oct 18, 2024
Neurodegenerative diseases (NDs) pose serious healthcare challenges with limited therapeutic treatments and high social burdens. The integration of artificial intelligence (AI) into drug discovery has emerged as a promising approach to address these ...
Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Oct 16, 2024
The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control,...
Data-augmented machine learning scoring functions for virtual screening of YTHDF1 mA reader protein.
Computers in biology and medicine
Oct 12, 2024
Machine learning is rapidly advancing the drug discovery process, significantly enhancing speed and efficiency. Innovation in computer-aided drug design is primarily driven by structure- and ligand-based approaches. When the number of known inhibitor...
A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.
Molecular diversity
Oct 9, 2024
The SARS-CoV-2 outbreak highlights the persistent vulnerability of humanity to epidemics and emerging microbial threats, emphasizing the lack of time to develop disease-specific treatments. Therefore, it appears beneficial to utilize existing resourc...
Drug-Target Binding Affinity Prediction in a Continuous Latent Space Using Variational Autoencoders.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 9, 2024
Accurate prediction of Drug-Target binding Affinity (DTA) is a daunting yet pivotal task in the sphere of drug discovery. Over the years, a plethora of deep learning-based DTA models have emerged, rendering promising results in predicting the binding...
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