Applications of machine learning in drug discovery and development
Drug discovery and development pipelines are long, complex and depend on numerous
factors. Machine learning (ML) approaches provide a set of tools that can improve discovery …
factors. Machine learning (ML) approaches provide a set of tools that can improve discovery …
Deep learning in chemistry
Machine learning enables computers to address problems by learning from data. Deep
learning is a type of machine learning that uses a hierarchical recombination of features to …
learning is a type of machine learning that uses a hierarchical recombination of features to …
Convolutional networks on graphs for learning molecular fingerprints
We introduce a convolutional neural network that operates directly on graphs. These
networks allow end-to-end learning of prediction pipelines whose inputs are graphs of …
networks allow end-to-end learning of prediction pipelines whose inputs are graphs of …
Generating focused molecule libraries for drug discovery with recurrent neural networks
In de novo drug design, computational strategies are used to generate novel molecules with
good affinity to the desired biological target. In this work, we show that recurrent neural …
good affinity to the desired biological target. In this work, we show that recurrent neural …
Low data drug discovery with one-shot learning
Recent advances in machine learning have made significant contributions to drug discovery.
Deep neural networks in particular have been demonstrated to provide significant boosts in …
Deep neural networks in particular have been demonstrated to provide significant boosts in …
DeepTox: toxicity prediction using deep learning
The Tox21 Data Challenge has been the largest effort of the scientific community to compare
computational methods for toxicity prediction. This challenge comprised 12,000 …
computational methods for toxicity prediction. This challenge comprised 12,000 …
AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery
Deep convolutional neural networks comprise a subclass of deep neural networks (DNN)
with a constrained architecture that leverages the spatial and temporal structure of the …
with a constrained architecture that leverages the spatial and temporal structure of the …
[HTML][HTML] CADD, AI and ML in drug discovery: A comprehensive review
D Vemula, P Jayasurya, V Sushmitha, YN Kumar… - European Journal of …, 2023 - Elsevier
Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest
because of its potential to expedite and lower the cost of the drug development process …
because of its potential to expedite and lower the cost of the drug development process …
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
Motivation While drug combination therapies are a well-established concept in cancer
treatment, identifying novel synergistic combinations is challenging due to the size of …
treatment, identifying novel synergistic combinations is challenging due to the size of …
[HTML][HTML] Artificial intelligence and machine learning in drug discovery and development
V Patel, M Shah - Intelligent Medicine, 2022 - Elsevier
The current rise of artificial intelligence and machine learning has been significant. It has
reduced the human workload improved quality of life significantly. This article describes the …
reduced the human workload improved quality of life significantly. This article describes the …