AIMC Topic: Drug Design

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Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers.

Journal of advanced research
INTRODUCTION: Small-molecule Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD1/PDL1) inhibition via PDL1 dimerization has the potential to lead to inexpensive drugs with better cancer patient outcomes and milder side effects. However...

MedGAN: optimized generative adversarial network with graph convolutional networks for novel molecule design.

Scientific reports
Generative Artificial Intelligence can be an important asset in the drug discovery process to meet the demand for novel medicines. This work outlines the optimization and fine-tuning steps of MedGAN, a deep learning model based on Wasserstein Generat...

AI for targeted polypharmacology: The next frontier in drug discovery.

Current opinion in structural biology
In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise ...

Generative AI for graph-based drug design: Recent advances and the way forward.

Current opinion in structural biology
Discovering new promising molecule candidates that could translate into effective drugs is a key scientific pursuit. However, factors such as the vastness and discreteness of the molecular search space pose a formidable technical challenge in this qu...

The emergence of machine learning force fields in drug design.

Medicinal research reviews
In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, ma...

T-MGCL: Molecule Graph Contrastive Learning Based on Transformer for Molecular Property Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
In recent years, machine learning has gained increasing traction in the study of molecules, enabling researchers to tackle challenging tasks including molecular property prediction and drug design.Consequently, there remains an open challenge to deve...

Deep learning-based design and screening of benzimidazole-pyrazine derivatives as adenosine A receptor antagonists.

Journal of biomolecular structure & dynamics
The Adenosine A receptor (AAR) is considered a novel potential target for the immunotherapy of cancer, and AAR antagonists have an inhibitory effect on tumor growth, proliferation, and metastasis. In our previous studies, we identified a class of ben...

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.

Nature reviews. Drug discovery
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rap...

Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence-II.

Biomolecules
Building on our 2021-2022 Special Issue, "Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence [...].

Drug discovery: In silico dry data can bypass biological wet data?

British journal of pharmacology
The recent and extraordinary increase in computer power, along with the availability of efficient algorithms based on artificial intelligence, has prompted a large number of inexperienced scientists to challenge the complex and yet competitive world ...