AIMC Topic: Drug Design

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AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors.

Scientific reports
The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for...

Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES.

Journal of computer-aided molecular design
Using generative deep learning models and reinforcement learning together can effectively generate new molecules with desired properties. By employing a multi-objective scoring function, thousands of high-scoring molecules can be generated, making th...

The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI.

New biotechnology
Deep learning has already revolutionised the way a wide range of data is processed in many areas of daily life. The ability to learn abstractions and relationships from heterogeneous data has provided impressively accurate prediction and classificati...

A Simple Way to Incorporate Target Structural Information in Molecular Generative Models.

Journal of chemical information and modeling
Deep learning generative models are now being applied in various fields including drug discovery. In this work, we propose a novel approach to include target 3D structural information in molecular generative models for structure-based drug design. Th...

Structure-Based Drug Discovery with Deep Learning.

Chembiochem : a European journal of chemical biology
Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and chemical biology, for example, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules de novo. While most of th...

Artificial Intelligence for Computer-Aided Drug Discovery.

Drug research
The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribut...

DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space.

Journal of chemical information and modeling
The discovery of novel molecules with desirable properties is a classic challenge in medicinal chemistry. With the recent advancements of machine learning, there has been a surge of drug design tools. However, few resources exist that are user-frien...

Application of message passing neural networks for molecular property prediction.

Current opinion in structural biology
Accurate molecular property prediction, as one of the classical cheminformatics topics, plays a prominent role in the fields of computer-aided drug design. For instance, property prediction models can be used to quickly screen large molecular librari...

Design of Nurr1 Agonists Fragment-Augmented Generative Deep Learning in Low-Data Regime.

Journal of medicinal chemistry
Generative neural networks trained on SMILES can design innovative bioactive molecules . These so-called chemical language models (CLMs) have typically been trained on tens of template molecules for fine-tuning. However, it is challenging to apply CL...