-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathindex.html
184 lines (153 loc) · 9.85 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Xiaoyang Qu - CV</title>
<meta name="theme-color" content="#ffc91d"/>
<link href="kico.css" rel="stylesheet" type="text/css"/>
<link href="moreduo.css" rel="stylesheet" type="text/css"/>
<link href="https://cdn.bootcss.com/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" type="text/css"/>
<meta name="viewport" content="width=device-width, maximum-scale=1, initial-scale=1"/>
</head>
<body>
<aside class="sidebar">
<div class="avatar">
<img src="seacoffee.jpg" alt=""/>
</div>
<nav class="nav">
<a href="#info">Personal information</a>
<a href="#education">Education</a>
<a href="#research">Research experience</a>
<a href="#publications">Publications</a>
<a href="#conferences">Academic conferences</a>
<a href="#awards">Awards</a>
<a href="#skills">Skills</a>
</nav>
</aside>
<main>
<section id="info">
<div class="wrap">
<h2 class="title">Personal information</h2>
<div class="row">
<div class="col-l-6">
<p>Name: Xiaoyang Qu</p>
<p>Address: Putian university, Fujian, China. 361005</p>
<p>Email: [email protected]</p>
<p>GitHub：<a href="https://github.com/xiaoyangqu/">https://github.com/xiaoyangqu/</a></p>
</div>
<div class="col-l-6">
<p>My research interests lie in developing physical and machine learning methods to untangle some biochemical problems, such as prediction of protein-ligand interactions, virtual screening of drugs, prediction of drug resistance, target fishing, etc. Further, I hope to develop some physically rigorous methods, and practical artificial intelligence tools, which provides new means for biochemistry research.</p>
</div>
</div>
</div>
</section>
<section id="education">
<div class="wrap">
<h2 class="title">Education</h2>
<div class="row">
<div class="col-m-12">
<ul class="timeline">
<li>2019-2023：<b><font color='SteelBlue '>Xiamen University</font></b>
<br>PhD candidates of Physical Chemistry</br>
<br>• Supervisor: Prof. BinjuWang</br>
</li>
<li>2016-2019：<b><font color='SteelBlue '>Zhengzhou University</font></b>
<br>Master Degree Candidate of chemical Engineering</br>
<br>• Supervisor: Guoli Zhou</br>
</li>
</ul>
</div>
</div>
</div>
</section>
<section id="research">
<div class="wrap">
<h2 class="title">Research experience during PhD</h2>
<div class="row">
<div class="col-m-12">
<b><font color='SteelBlue '>Development of methods for predicting protein-ligand interactions:</font></b>
<ul class="timeline">
<li><b><font color='SteelBlue '>Polarization force field-based method</font></b></li>
• Construction of protein residue-ligand interaction energy (quantum mechanical level) datasets using a chunking approach. </br>
• Introduction of an electrostatic intrusion model to correct the AMOEBA polarization force field (i.e., the AMOEBA_CP force field that takes into account quantum effects at close range).. </br>
• Re-parameterizing the force field using the constructed quantum mechanical energy data set. </br>
• Combined with multi-step simulated annealing (molecular dynamics approach) to successfully predict the change in inhibitor binding affinity by 144 amino acid mutations in the cancer target Abl kinase. </br>
<br></br>
<li><b><font color='SteelBlue '>Machine learning-based method</font></b></li>
• The development of new machine learning descriptors based on water molecule networks at protein-ligand binding interfaces and the interaction of water and complexes. </br>
• Systematic enhancement of multiple classical machine learning computational methods using the new descriptors. </br>
• New features combined with models trained under different algorithms are able to reliably predict interactions, predict molecular binding conformations as well as screen bindable molecules. </br>
</ul>
</div>
<section id="publications">
<div class="wrap">
<h2 class="title">Publications</h2>
<div class="row">
<div class="col-m-12">
<ol start='value'>
<li><font color='SteelBlue '><b>Xiaoyang Qu</b></font>, Lina Dong, Ding Luo, Yubing Si, Binju Wang<sup>*</sup>, <b>Water Network-Augmented Two-State Model for Protein−Ligand Binding Affinity Prediction</b>, <b>Journal of Chemical Information and Modeling</b>, 2023, <a href="https://doi.org/10.1021/acs.jcim.3c00567">https://doi.org/10.1021/acs.jcim.3c00567</a> (<b>JCR Q1</b>)</li>
<li><font color='SteelBlue '><b>Xiaoyang Qu</b></font>, Lina Dong, Jinyan Zhang, Yubing Si, Binju Wang<sup>*</sup>, <b>Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules</b>, <b>Journal of Chemical Information and Modeling</b>, 2022, <a href="https://doi.org/10.1021/acs.jcim.2c00916">https://doi.org/10.1021/acs.jcim.2c00916</a> (<b>JCR Q1</b>)</li>
<li><font color='SteelBlue '><b>Xiaoyang Qu</b></font>, Lina Dong, Yubing Si, Yuan Zhao, Qiantao Wang<sup>*</sup>, Peifeng Su, and Binju Wang<sup>*</sup>, <b>Reliable Prediction of the Protein−Ligand Binding Affinity Using a Charge Penetration Corrected AMOEBA Force Field: A Case Study of Drug Resistance Mutations in Abl Kinase</b>, <b>Journal of Chemical Theory and Computation</b>, 2022, <a href="https://doi.org/10.1021/acs.jctc.1c01005">https://doi.org/10.1021/acs.jctc.1c01005</a> (<b>JCR Q1</b>)</li>
<li><font color='SteelBlue '><b>Xiaoyang Qu</b></font>, Guoli Zhou, Yijun Cao, Peng Li, Yuyuan He, Jie Zhang, <b>Synergetic effect on the combustion of lignite blended with humus: Thermochemical characterization and kinetics</b>, <b>Applied Thermal Engineering journal</b>, 2019, <a href="https://doi.org/10.1016/j.applthermaleng.2019.02.026">https://doi.org/10.1016/j.applthermaleng.2019.02.026</a> (<b>JCR Q1</b>)</li>
<li>Wei Peng, <font color='SteelBlue '><b>Xiaoyang Qu</b></font>, Sason Shaik<sup>*</sup>, and Binju Wang<sup>*</sup>, <b>Deciphering the oxygen activation mechanism at the Cu<sub>C</sub> site of particulate methane monooxygenase</b>, <b>Nature Catalysis</b>, 2021, <a href="https://doi.org/10.1038/s41929-021-00591-4">https://doi.org/10.1038/s41929-021-00591-4</a> (<b>JCR Q1</b>)</li>
<li>Lina Dong, <font color='SteelBlue '><b>Xiaoyang Qu</b></font>, Binju Wang<sup>*</sup>, <b>XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein–Ligand Scoring and Ranking</b>, <b>ACS OMEGA</b>, 2022, <a href="https://doi.org/10.1021/acsomega.2c01723">https://doi.org/10.1021/acsomega.2c01723</a> (<b>JCR Q2</b>)</li>
<li>Lina Dong, <font color='SteelBlue '><b>Xiaoyang Qu</b></font>, Yuan Zhao, Binju Wang<sup>*</sup>, <b>Prediction of Binding Free Energy of Protein-Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method</b>, <b>ACS OMEGA</b>, 2021, <a href="https://doi.org/10.1021/acsomega.1c04996">https://doi.org/10.1021/acsomega.1c04996</a> (<b>JCR Q2</b>)</li>
</ol>
</div>
</div>
</div>
</section>
<section id="conferences">
<div class="wrap">
<h2 class="title">Academic conferences</h2>
<div class="row">
<div class="col-m-12">
<ul class="timeline">
<li><b>2022 The 11th Workshop on Computational Statistical Mechanics of Complex Systems</b></li>
<font color='RoyalBlue'>Excellent Poster presentation</font></br></br>
<li><b>2021 International Symposium on Machine Learning in Quantum Chemistry</b></li>
</ul>
</div>
</section>
<section id="awards">
<div class="wrap">
<h2 class="title">Awards</h2>
<div class="row">
<div class="col-m-12">
<ul class="timeline">
<li><b>2022 Merit student of Xiamen university</b></li>
<li><b>2022 Excellence Award of Xiamen Quantum Chemistry Forum</b></li>
<li><b>2022 "Zhao Yufen-Zhejiang Yongning Pharma" Scholarship</b></li>
</ul>
</div>
</section>
<section id="skills">
<div class="wrap">
<h2 class="title">Skills</h2>
<div class="row">
<div class="col-m-12">
<ul class="timeline">
<li><b>Development & Language</b>
<br>Python, Linux(Shell), Ruby, Matlab, SQL, Latex, Markdown, etc</br>
</li>
<li><b>Software</b>
<br>Gaussian(QM), PSI4, MOPAC, GAMESS-US, VASP, AMEBR(MD), Tinker(force field development), Schrodinger, Sybyl, AutoDockVina, Smina, Gnina(molecule docking) PyMOL(Molecular property calculation&&visualized analysis), Rosetta, SCWRL4(Enzemy design), etc</br>
</li>
<li><b>Framework</b>
<br>Pytorch, Pytorch-geometric, Scikit-learn, Tensorflow, Keras, Scikit-multilearn, XGBoost, Networkx, Requests, Flask, etc</br>
</li>
<li><b>Laboratory instruments</b>
<br>Thermal gravimetric analyzer, Brunner−Emmet−Teller(BET) measurements, X-ray diffractometer, FT-IR Spectrometer, FT-Raman Spectrometer, UV-Visible Spectrophotometer, etc</br>
</li>
</ul>
</div>
</section>
<footer>
<p>© 2022.10.1</p>
</footer>
</main>
<script src="kico.js"></script>
<script src="moreduo.js"></script>
</body>
</html>
<!--https://xiaoyangqu.github.io/cv/-->