Project Team

Qifeng Bai

School of Basic Medical Sciences
Lanzhou University
No. 222 South Tianshui Road
Lanzhou 730000, Gansu Province, P.R.China
Homepage: https://molaical.github.io
Email: molaical@yeah.net

Horacio Pérez Sánchez

Computer Engineering Department
Universidad Católica San Antonio de Murcia
Murcia, Spain
Homepage: http://bio-hpc.eu
Email: hperez@ucam.edu


Tingyang Xu

Tencent AI Lab
Shenzhen, 518057, P. R. China.
Homepage: https://ai.tencent.com/ailab/en/index
Email: tingyangxu@tencent.com



Junzhou Huang

Tencent AI Lab and UTA
Shenzhen, 518057, P. R. China.
Homepage: https://ai.tencent.com/ailab/en/index
Email: joehhuang@tencent.com




Citing WADDAICA

If WADDAICA is used in your work, please cite the below two papers:

1. Bai, Q.*, Ma, J., Liu, S., Xu, T., Banegas-Luna, A. J., Pérez-Sánchez, H.*, et al. WADDAICA: A webserver for aiding protein drug design by artificial intelligence and classical algorithm. Computational and Structural Biotechnology Journal 19, 3573-3579, (2021).
https://doi.org/10.1016/j.csbj.2021.06.017             Download: [EndNote style]

2. Bai, Q., Research and development of MolAICal for drug design via deep learning and classical programming. arXiv 2020. doi: https://arxiv.org/abs/2006.09747             Download: [EndNote style]

Acknowledgement

We are grateful to "Tencent AI Lab Rhino-Bird Focused Research Program (No. JR202004)" who supports the grant for this project. This work was partially supported by grants from the Fundación Séneca del Centro de Coordinación de la Investigación de la Región de Murcia (Spain) under Project 20988/PI/18, Spanish Ministry of Science and Innovation under Project CTQ2017-87974-R, and by European Project Horizon 2020 SC1-BHC-02-2019 [REVERT, ID:848098]. We thank the authors and projects of AutoDock Vina [1], and MolAICal [2], pafnucy [3], onionnet [4], ligdream [5], Chemistry Development Kit [6], Open Babel [7], JSME [8] and Jmol (http://www.jmol.org) for sharing their licenses.


References

1. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, Journal of Computational Chemistry 2010;31:455-461.
2. Bai Q, Tan S, Xu T et al. MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm, Brief Bioinform 2020.
3. Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P. Development and evaluation of a deep learning model for protein-ligand binding affinity prediction, Bioinformatics 2018;34:3666-3674.
4. Zheng L, Fan J, Mu Y. OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein-Ligand Binding Affinity Prediction, ACS Omega 2019;4:15956-15965.
5. Skalic M, Jimenez J, Sabbadin D et al. Shape-Based Generative Modeling for de Novo Drug Design, Journal of Chemical Information and Modeling 2019;59:1205-1214.
6. Willighagen EL, Mayfield JW, Alvarsson J et al. The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching, Journal of Cheminformatics 2017;9:33.
7. O'Boyle NM, Banck M, James CA et al. Open Babel: An open chemical toolbox, Journal of Cheminformatics 2011;3:33.
8. Bienfait B, Ertl P. JSME: a free molecule editor in JavaScript, Journal of Cheminformatics 2013;5:24.