[HTML][HTML] De novo molecular design and generative models

J Meyers, B Fabian, N Brown - Drug discovery today, 2021 - Elsevier
Molecular design strategies are integral to therapeutic progress in drug discovery.
Computational approaches for de novo molecular design have been developed over the …

LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design

V Fialková, J Zhao, K Papadopoulos… - Journal of Chemical …, 2021 - ACS Publications
Because of the strong relationship between the desired molecular activity and its structural
core, the screening of focused, core-sharing chemical libraries is a key step in lead …

REINVENT 2.0: an AI tool for de novo drug design

T Blaschke, J Arús-Pous, H Chen… - Journal of chemical …, 2020 - ACS Publications
In the past few years, we have witnessed a renaissance of the field of molecular de novo
drug design. The advancements in deep learning and artificial intelligence (AI) have …

Discovery of senolytics using machine learning

V Smer-Barreto, A Quintanilla, RJR Elliott… - Nature …, 2023 - nature.com
Cellular senescence is a stress response involved in ageing and diverse disease processes
including cancer, type-2 diabetes, osteoarthritis and viral infection. Despite growing interest …

Interpretation of structure–activity relationships in real-world drug design data sets using explainable artificial intelligence

T Harren, H Matter, G Hessler, M Rarey… - Journal of Chemical …, 2022 - ACS Publications
In silico models based on Deep Neural Networks (DNNs) are promising for predicting
activities and properties of new molecules. Unfortunately, their inherent black-box character …

De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization

A Domenico, G Nicola, T Daniela, C Fulvio… - Journal of Chemical …, 2020 - ACS Publications
Artificial intelligence and multiobjective optimization represent promising solutions to bridge
chemical and biological landscapes by addressing the automated de novo design of …

Prioritizing virtual screening with interpretable interaction fingerprints

AV Fassio, L Shub, L Ponzoni, J McKinley… - Journal of Chemical …, 2022 - ACS Publications
Machine learning-based drug discovery success depends on molecular representation. Yet
traditional molecular fingerprints omit both the protein and pointers back to structural …

Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations

B Ma, K Terayama, S Matsumoto, Y Isaka… - Journal of Chemical …, 2021 - ACS Publications
Recently, molecular generation models based on deep learning have attracted significant
attention in drug discovery. However, most existing molecular generation models have …

Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry

SA Kumar, TD Ananda Kumar… - Future Medicinal …, 2022 - Taylor & Francis
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead
optimization in drug discovery research, requires molecular representation. Previous reports …

A highly accurate metadynamics-based Dissociation Free Energy method to calculate protein–protein and protein–ligand binding potencies

J Wang, A Ishchenko, W Zhang, A Razavi… - Scientific Reports, 2022 - nature.com
Although seeking to develop a general and accurate binding free energy calculation method
for protein–protein and protein–ligand interactions has been a continuous effort for decades …