What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Biochemistry
His main research concerns Data science, Biochemistry, Molecular physics, Drug discovery and Data mining. His work in Data science addresses issues such as Chemogenomics, which are connected to fields such as Drug. His Drug discovery study frequently links to related topics such as Artificial neural network.
His Artificial neural network study is concerned with Artificial intelligence in general. His biological study spans a wide range of topics, including Machine learning and Datasets as Topic. His research integrates issues of PubChem and chEMBL in his study of Data mining.
His most cited work include:
- The rise of deep learning in drug discovery. (456 citations)
- Molecular de-novo design through deep reinforcement learning (288 citations)
- Accurate Intermolecular Potentials Obtained from Molecular Wave Functions: Bridging the Gap between Quantum Chemistry and Molecular Simulations (142 citations)
What are the main themes of his work throughout his whole career to date?
Ola Engkvist mainly focuses on Artificial intelligence, Drug discovery, Computational biology, Data mining and Deep learning. His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Pattern recognition. His Drug discovery study combines topics from a wide range of disciplines, such as Combinatorial chemistry, Nanotechnology, Pharmaceutical industry and Data science.
His work focuses on many connections between Data science and other disciplines, such as Big data, that overlap with his field of interest in Cheminformatics. His Data mining research includes elements of Pharmacophore, Similarity, Selection and chEMBL. His Recurrent neural network research incorporates elements of Chemical space, Generative model and Reinforcement learning.
He most often published in these fields:
- Artificial intelligence (24.00%)
- Drug discovery (22.67%)
- Computational biology (12.67%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (24.00%)
- Deep learning (11.33%)
- Machine learning (10.67%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Deep learning, Machine learning, Drug discovery and Recurrent neural network. His work in the fields of Generative grammar and Artificial neural network overlaps with other areas such as Set, CASP and Field. Ola Engkvist has researched Deep learning in several fields, including Theoretical computer science, Cheminformatics, Molecular descriptor, chEMBL and Hyperparameter.
His Cheminformatics research is multidisciplinary, relying on both Pharmaceutical industry, Data science and Big data. His study in Management science extends to Drug discovery with its themes. Ola Engkvist focuses mostly in the field of Recurrent neural network, narrowing it down to matters related to Chemical space and, in some cases, Reinforcement learning, Chemical database and Generative model.
Between 2018 and 2021, his most popular works were:
- Randomized SMILES strings improve the quality of molecular generative models (44 citations)
- Exploring the GDB-13 chemical space using deep generative models (41 citations)
- A de novo molecular generation method using latent vector based generative adversarial network (37 citations)
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