Computer-based de novo design of drug-like molecules

By Gisbert Schneider, Uli Fechner. 2005

871 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Organic Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Gisbert Schneider
2. Jean-Louis Reymond
3. Ruud Van Deursen
4. Lorenz C. Blum
5. Lars Ruddigkeit
6. Francesca Grisoni
7. Jacob D. Durrant
8. James Andrew McCammon

In Molecular Biology

1. Jean-Louis Reymond
2. Ruud Van Deursen
3. Lorenz C. Blum
4. Jacob D. Durrant
5. Lars Ruddigkeit
6. Gisbert Schneider
7. Wenbo Yu
8. Francesca Grisoni

In Organic Chemistry

1. Jean-Louis Reymond
2. Ruud Van Deursen
3. Lorenz C. Blum
4. Wenbo Yu
5. Alexander D. MacKerell
6. James Andrew McCammon
7. Jacob D. Durrant
8. R. Amaro

Citations in the corpus

• SWEETLEAD: an In Silico Database of Approved Drugs, Regulated Chemicals, and Herbal Isolates for Computer-Aided Drug Discovery (2013, neutral): “screening[12-14].”
• Molecular de-novo design through deep reinforcement learning (2017, methodology): “lgorithms to virtually design and assess molecules, de novo design oers ways to reduce the chemical space into something more manageable for the search of the needle. Early de novo design algorithms [1] used structure based approaches to grow ligands to sterically and electronically t the binding po”
• Multi-objective de novo drug design with conditional graph generative model (2018, background): “It is estimated that there are around 10(60)−10(100) synthetically available molecules [1].”
• Assessing protein–ligand interaction scoring functions with the CASF-2013 benchmark (2018, methodology): “In fact, scoring functions are also applied to other tasks, such as automatic de novo ligand design 17,18 .”
• Artificial intelligence in drug discovery: recent advances and future perspectives (2021, background): “De novo design, the generation of novel molecular entities with desired pharmacological properties from scratch [109], can be considered as one of the most challenging computer-assisted tasks in drug discovery, due to the cardinality of the chemical space of drug-like molecules (estimated to range…”
• Graph convolutional networks for computational drug development and discovery (2019, background): “However, drug design in reality is difficult and expensive due to virtually infinite search space [75].”