AIMC Topic: Drug Discovery

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Identifying predictive features in drug response using machine learning: opportunities and challenges.

Annual review of pharmacology and toxicology
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction ...

Active-learning strategies in computer-assisted drug discovery.

Drug discovery today
High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the s...

Machine-learning approaches in drug discovery: methods and applications.

Drug discovery today
During the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and ...

Machine learning-driven discovery of antimicrobial peptides targeting the GAPDH-TPI protein-protein interaction in Schistosoma mansoni for novel antischistosomal therapeutics.

Computational biology and chemistry
Schistosomiasis, caused by Schistosoma mansoni, remains a significant public health burden, particularly in endemic regions with limited access to effective treatment. The emergence of resistance to praziquantel necessitates the urgent discovery of n...

Bridging BioSciences and technology: The impact of AI & GenAI in life sciences and agribusiness.

Gene
The intersection of biosciences and technology has yielded transformative advancements, and Generative Artificial Intelligence (GenAI) started to stand at the forefront of this synergy. In the field of life sciences, GenAI is emerging as a catalyst, ...

Integrative Computational Approaches for TRPV1 Ion Channel Inhibitor Discovery: An Integrated Machine Learning, Drug Repurposing and Molecular Simulation Approach.

Journal of chemical information and modeling
The transient receptor potential vanilloid 1 (TRPV1) ion channel is a key mediator of pain and inflammation, making it a crucial target for developing new analgesics. Despite progress in understanding TRPV1's role, novel modulators that effectively i...

BiVAE-CPI: An Interpretable Generative Model Using a Bilateral Variational Autoencoder for Compound-Protein Interaction Prediction.

Journal of chemical information and modeling
Predicting compound-protein interaction (CPI) plays a critical role in drug discovery and development, but traditional screening experiments consume much time and resources. Therefore, deep learning methods for CPI prediction are popular now. However...

Multilevel Fusion Graph Neural Network for Molecule Property Prediction.

Journal of chemical information and modeling
Accurate prediction of molecular properties is essential in drug discovery and related fields. However, existing graph neural networks (GNNs) often struggle to simultaneously capture both local and global molecular structures. In this work, we propos...

AMPGP: Discovering Highly Effective Antimicrobial Peptides via Deep Learning.

Journal of chemical information and modeling
Antimicrobial peptides (AMPs) have emerged as vital candidates in the fight against antibiotic resistance. The traditional processes for AMP design and discovery are often time-consuming and inefficient. Here, we propose the AMPGP model, which employ...

AI-Designed Molecules in Drug Discovery, Structural Novelty Evaluation, and Implications.

Journal of chemical information and modeling
Achieving structural novelty in drug discovery remains a critical challenge. Artificial intelligence (AI) has demonstrated remarkable potential in deciphering the complex relationships between molecular structures and activities from vast amounts of ...