1. Structure-based drug design (SBDD)
Early CADD scientists lacked structural information about biomolecules, so the primary choice was to use only experimental data from ligands from which enough information could be inferred to build reliable quantitative structure-activity relationship (QSAR) models, which is also called ligand-based drug design (LBDD). However, with the development of structural biology, including nuclear magnetic resonance (NMR), X-ray crystallography (XR), and cryogenic electron microscopy (cryo-EM), scientists can use structural information of biomolecules for drug design through computational methods, including molecular dynamics (MD) and docking. All these technologies belong to SBDD.
There are 3 main points I'm extremely interested in this field:
a. Rational design of agonists and antagonists.
b. Generative models for small molecular drugs and peptides.
c. Drug molecule database
2. High-performance computing (HPC) in computational biophysics
Atomistic simulations of biomolecules provide a detailed view of the structure and dynamics that complement experiments. Increased conformational sampling, enabled by new algorithms and growth in computer power, now allows a much broader range of events to be observed, providing critical insights largely inaccessible to experiments. In the last few years, the successful application of graphics process units (GPUs) and super-computer like Anton to MD simulations has dramatically extended our ability to simulate very big systems (>100,000 atoms) at a relative longtime scale (microseconds). Over long enough periods of time, MD tools like AMBER are still more accurate than AI tools like AF2. Another important thing is that, with the development of AI, especially for LLM and generative models used in science, HPC is becoming more and more important in accelerating drug discovery.
In addition to GPUs, some specific hardware was designed to meet the requirement for super computing, including LPUs for Transformer, Anton for MD and quantum computers for QM. The trend of future is HPC!
Currently, I'm using MPI, openMP and CUDA to do some little project on physical simulation projects.
3. Bayesian theory in life and drug design
Bayesian theory, a branch of statistics, is based on the concept of probability as a measure of belief or confidence. It allows for the incorporation of prior knowledge, along with the current evidence in decision-making. It has been widely used in various fields, including machine learning and data science.
In relation to drug design, Bayesian models can be used to predict the properties of molecules or simulate their behavior, speeding up the process of drug discovery. The advantage of Bayesian methods is their capacity to quantify uncertainty, which is crucial in the early stages of drug design where decisions are made based on limited data.
I'm particularly interested in how Bayesian methods can be applied to improve the efficiency and accuracy of drug design and explain abnormal phenomena in life.
4. Powerlifting
I'm a powerlifter, and my goal is to become the strongest man both mentally and physically!
HIV-1 RNA phase separation
Mini-guts culture and understanding the mechanism of JAK3 inhibitors in treating Inflammatory bowel diseases (IBD)
From traditional dual-targeted radiopharmaceuticals to and-logic controlled new drugs
Covalent nuclear pharmaceuticals - a new, powerful weapon in oncotherapy
Organotrifluoroborate-modified fibroblast activation protein inhibitors (FAPIs) for Targeted Radionuclide Therapy
Yu, Liu.; Haocheng, Tang.; Tianchi, Song.; Mengxin, Xu.; Junyi, Chen.; Xi-Yang, Cui.; Yuxiang, Han.; Zhu, Li.; Zhibo, Liu. Organotrifluoroborate Enhances Tumour Targeting of Fibroblast Activation Protein Inhibitors for Targeted Radionuclide Therapy. Eur J Nucl Med Mol Imaging, 2023, 10.1007/s00259-023-06230–06233.
Qin Wanshun-Jin Yunhui Scholarship for Outstanding Academic Performance, Peking University (top 10%) 2023
Merit Student Award, Peking University (top 10%) 2023
Principal’s Fund (Science) for Undergraduate Research, Peking University 2023
Third Prize, “New Synthesis” Popular Science Article Contest 2021
Silver Medal, 33th Chinese Chemistry Olympiad 2019