Designing molecules by optimizing potentials.

By Mingliang Wang, Xiangqian Hu, David N. Beratan, Weitao Yang. 2006

130 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, 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 Atomic and Molecular Physics, and Optics

1. Jacob D. Durrant
2. Pavlo O. Dral
3. R. Ramakrishnan
4. von Lilienfeld
5. James Andrew McCammon
6. Germany O. Anatole
7. Max-Planck-Institut f¨ur Kohlenforschung
8. Matthias Rupp 

In Organic Chemistry

1. James Andrew McCammon
2. R. Amaro
3. Germany O. Anatole
4. Max-Planck-Institut f¨ur Kohlenforschung
5. Jacob D. Durrant
6. Matthias Rupp 
7. von Lilienfeld
8. Pavlo O. Dral

Citations in the corpus

• Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. (2015, background): “To achieve the overall goal of de novo in silico molecular and materials design [12–15], however, substantial progress is still necessary [16], especially regarding prediction accuracy, computational speed, and transferability of the employed models.”
• AutoGrow: A Novel Algorithm for Protein Inhibitor Design (2009, background): “Even so, physics-based ligand optimization methods based on density functional theory (6) and electron-nuclear attraction potentials (7) are promising new approaches.”