As a powerful data mining and analysis technology, AI has been involved in most stages of drug research and development, and may promote substantial breakthroughs in the fields of lead screening/design and evaluation. However, several critical issues still need be addressed for AI-based data-driven innovative drug design and screening methods. The core research direction of our group is to develop accurate and efficient virtual screening methods, drugability prediction methods, de novel drug design methods, retrosynthesis prediction methods and target prediction methods by applying big data and AI technology, and then develop an integrated AI-based software platform for high-precision drug design and screening. For example, we developed KGE-NFM, a drug-target interaction prediction method based on knowledge graph and recommendation system, which showed better prediction accuracy than traditional methods on multiple benchmark datasets ; we developed MCMG, a new method for multi-constraint molecule generation by knowledge distillation and reinforcement learning, which can efficiently traverse complicated chemical space to discover novel compounds that satisfy multiple property constraints, providing a powerful computational tool for the discovery of lead structures ( Figure 1) [2~3]; we developed IGN, a novel protein-small molecule interaction scoring method based on graph representation learning, and its performance is significantly better than those of similar machine learning scoring methods and molecular docking programs ; we systematically studied several key issues in machine learning scoring functions, and developed an online computational platform ASFP for protein-small molecule interaction feature extraction, personalized scoring function construction, and virtual screening [4~8]; we proposed Computational Bioactivity Fingerprint (CBFP), and used it as a new type of molecular representation for scaffold-hopping and successfully discovered potent PARP1 and DprE1 inhibitors with novel frameworks [9~10].
Figure 1. Workflow diagram of the MCMG method.
We have developed a variety of theoretical models for predicting important ADMET properties, such as aqueous solubility (logS), octanol/water partition coefficient (logP), Caco-2 permeability, intestinal absorption, oral bioavailability, hERG blockade, blood-brain barrier (BBB), P-glycoprotein transporting, pregnane X receptor activation, respiratory toxicity, urinary toxicity, acute toxicity, frequent hitters, drug-likeness, etc. [11~16]
Because small sample size for druggability makes it difficult to achieve accurate predictions, new AI technologies such as transfer learning, multi-task learning, and self-supervised learning have been used to develop fast and accurate druggability prediction models (such as multi-task graph attention framework MGA and pre-trained graphs network models MG-BERT and K-BERT), which can effectively improve the prediction accuracy of multiple druggability tasks, especially for tasks with fewer samples [17~19]. Moreover, an online software platform called ADMETLab2.0 was developed to predict a number of important ADMET parameters, including 17 kinds of physicochemical properties, 13 kinds of medicinal properties, 24 kinds of druggability parameters, 27 kinds of toxicity properties and 8 kinds of toxic group rules. It is one of most powerful platforms for ADMET predictions .
Figure 2. The prediction result display page of ADMETlab 2.0.
1. Qing Ye, Chang-Yu Hsieh, Ziyi Yang, Yu Kang, Jiming Chen, Dongsheng Cao*, Shibo He*, Tingjun Hou*, A unified drug-target interaction prediction framework based on knowledge graph and recommendation system, Nature Communications, 2021, 12, 6775.
2. Jike Wang, Chang-Yu Hsieh, Mingyang Wang, Xiaorui Wang, Zhenxing Wu, Dejun Jiang, Benben Liao, Xujun Zhang, Bo Yang, Qiaojun He, Dongsheng Cao*, Xi Chen*, Tingjun Hou*, Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning, Nature Machine Intelligence, 2021, 3, 914-922.
3. Mingyang Wang, Zhe Wang, Huiyong Sun, Jike Wang, Chao Shen, Gaoqi Weng, Xin Chai, Honglin Li*, Dongsheng Cao*, Tingjun Hou*, Deep learning approaches for de novo drug design: an overview, Current Opinion in Structural Biology, 2022, 72, 135-144.
4. Dejun Jiang, Chang-Yu Hsieh, Zhenxing Wu, Yu Kang, Jike Wang, Ercheng Wang, Ben Liao, Chao Shen, Lei Xu, Jian Wu*, Dongsheng Cao*, Tingjun Hou*, InteractionGraphNet: a novel and efficient deep graph representation learning framework for accurate protein-ligand interaction predictions, Journal of Medicinal Chemistry, 2021, 64, 18209-18232.
5. Chao Shen, Ye Hu, Zhe Wang, Xujun Zhang, Haiyang Zhong, Gaoang Wang, Xiaojun Yao, Lei Xu, Dongsheng Cao*, Tingjun Hou*, Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions, Briefings in Bioinformatics, 2021, 22, 497-514.
6. Chao Shen, Ye Hu, Zhe Wang, Xujun Zhang, Jinping Pang, Gaoang Wang, Haiyang Zhong, Lei Xu, Dongsheng Cao*, Tingjun Hou*, Beware of the generic machine learning-based scoring functions in structure-based virtual screening, Briefings in Bioinformatics, 2021, 22, bbaa070.
7. Chao Shen, Junjie Ding, Zhe Wang, Dongsheng Cao, Xiaoqin Ding, Tingjun Hou*, From machine learning to deep learning: advances in scoring functions for computational docking, WIRES Computational Molecular Science, 2020, 10, e1429.
8. Xujun Zhang, Chao Shen, Xueying Guo, Zhe Wang, Gaoqi Weng, Qing Ye, Gaoang Wang, Qiaojun He, Bo Yang*, Dongsheng Cao*, Tingjun Hou*, ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions, Journal of Cheminformatics, 2021, 13, 6.
9. Guoli Xiong, Yue Zhao, Lu Liu, Zhongye Ma, Aiping Lu, Yan Cheng, Tingjun Hou*, Dongsheng Cao*, Computational bioactivity fingerprint similarities to navigate the discovery of novel scaffolds, Journal of Medicinal Chemistry, 2021, 64, 7544-7554.
10. Xueping Hu, Liu Yang, Xin Chai, Yixuan Lei, Md Shah Alam, Lu Liu, Chao Shen, Dejun Jiang, Zhe Wang, Zhiyong Liu, Lei Xu, Kanglin Wan, Tianyu Zhang, Yuelan Yin, Dongsheng Cao*, Dan Li*, Tingjun Hou*, Discovery of novel DprE1 inhibitors via computational bioactivity fingerprints and structure-based virtual screening, Acta Pharmacologica Sinica, 2022, in press.
11. Sheng Tian, Junmei Wang, Youyong Li, Dan Li, Lei Xu, Tingjun Hou*, The application of in silico drug-likeness predictions in pharmaceutical research, Advanced Drug Delivery Reviews, 2015, 86, 2-10.
12. 12. Tailong Lei, Huiyong Sun, Yu Kang, Feng Zhu. Hui Liu, Wenfang Zhou, Zhe Wang, Dan Li, Youyong Li, Tingjun Hou*, ADMET evaluation in drug discovery. 18. Reliable prediction of chemical-induced urinary tract toxicity by boosting machine learning approaches, Molecular Pharmaceutics,2017, 14, 3935-3953.
13. Tailong Lei, Fu Chen, Hui Liu, Huiyong Sun, Yu Kang, Dan Li, Youyong Li, Tingjun Hou*, ADMET evaluation in drug discovery. 17. Development of quantitative and qualitative prediction models for chemical-induced respiratory toxicity, Molecular Pharmaceutics, 2017, 14, 2407-2421.
14. Zhenxing Wu, Tailong Lei, Chao Shen, Zhe Wang, Dongsheng Cao, Tingjun Hou*, ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches, Journal of Chemical Information and Modeling, 2019, 59, 4587-4601.
15. 15. Ziyi Yang, Junhong He, Aiping Lu, Tingjun Hou*, Dongsheng Cao*, The Application of Negative Design to Design More Desirable Virtual Screening Library, Journal of Medicinal Chemistry, 2020, 63, 4411-4429.
16. Tailong Lei, Huiyong Sun, Yu Kang, Feng Zhu. Hui Liu, Wenfang Zhou, Zhe Wang, Dan Li, Youyong Li, Tingjun Hou*, ADMET evaluation in drug discovery. 18. Reliable prediction of chemical-induced urinary tract toxicity by boosting machine learning approaches, Molecular Pharmaceutics, 2017, 14, 3935-3953.
17. Zhenxing Wu, Dejun Jiang, Jike Wang, Chang-Yu Hsieh*, Dongsheng Cao*, Tingjun Hou*, Mining Toxicity Information from Large Amounts of Toxicity Data, Journal of Medicinal Chemistry, 2021, 64, 6924-6936.
18. Xiaochen Zhang, Chengkun Wu, Zhijiang Yang, Zhenxing Wu, Jiacai Yi, Changyu Hsieh, Tingjun Hou*, Dongsheng Cao*, MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction, Briefings in Bioinformatics, 2021, 22, bbab152.
19. Zhenxing Wu, Dejun Jiang, Jike Wang, Xujun Zhang, Hongyan Du, Lurong Pan, Changyu Hsieh*, Dongsheng Cao*, Tingjun Hou*, Knowledge-based BERT: a method to extract molecular features like computational chemists, Briefings in Bioinformatics, 2022, bbac131.
20. Guoli Xiong, Zhenxing Wu, Jiacai Yi, Li Fu, Zhijiang Yang, Changyu Hsieh, Mingzhu Yin, Xiangxiang Zeng, Chengkun Wu, Aiping Lu, Xiang Chen, Tingjun Hou*, Dongsheng Cao*, ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties, Nucleic Acids Research, 2021, 49(W1), W5-W14.
Due to the advantages of low cost and high efficiency, molecular docking-based virtual screening has become a key technology for drug discovery. Most scoring functions used in molecular docking are computationally efficient but have relatively low accuracy. Moreover, the effect of target protein flexibility is often ignored in docking-based virtual screening. In order to improve the prediction accuracy of virtual screening, we have conducted in-depth and systematic methodological research on target-ligand interaction predictions, developed a series of computational tools and software packages for drug design and screening, and then applied them to drug design towards multiple important targets. Since 2009, we have systematically explored several core theoretical issues in MM/PB(GB)SA, and focused on how to improve its predictive ability and computational efficiency in virtual screening. This series of studies deeply investigated the influence of various factors on the prediction accuracy of MM/GBSA, such as molecular force field, solvation model, atomic charge, conformational entropy, sampling time, etc., and proposed the optimal prediction strategies for different systems [1~11]. Considering the inherent defects that the traditional MM/GBSA method cannot effectively characterize the heterogeneous polar environment of protein-ligand interaction interfaces, a VD-MM/GBSA approach based on variable dielectric constants was developed. In VD-MM/GBSA, the dielectric constants are optimized according to the physicochemical properties and chemical environments of different residues, which can effectively improve the prediction accuracy of binding free energies [12~13]. Moreover, we developed a series of computational tools for drug design and screening based on MM/PB(GB)SA, such as CaFE, farPPI and HawkDock [14~16].
In order to overcome the system-dependent defect of traditional MM/GBSA method, a MIEC-SVM method by combining binding free energy decomposition and machine learning was developed and successfully applied to the study of peptide-protein interactions and the screening of small molecule inhibitors towards multiple targets [17-18]. Using different computational strategies, the impact of target conformational changes on virtual screening was discussed in depth, highlighting the importance of rationally handling target flexibility to accurately predict inhibitor binding mechanisms and improve the accuracy of virtual screening. The integrated virtual screening method based on multiple conformations has been successfully applied to the drug design and screening of multiple targets such as ALK, AR and ROCK1. It is proved that by integrating the prediction results of multiple conformations of the target, the flexibility of the target can be effectively considered, thereby significantly improving the prediction accuracy of virtual screening (Figure 3) [19-20].
Figure 3. Workflow of integrated VS protocol.
1. Ercheng Wang, Huiyong Sun, Junmei Wang, Zhe Wang, Hui Liu, John Z.H. Zhang*, Tingjun Hou*, End-Point binding free energy calculation with MM/PBSA and MM/GBSA: strategies and applications in drug design, Chemical Reviews, 2019, 119, 9478-9508.
2. Ercheng Wang, Gaoqi Weng, Huiyong Sun, Hongyan Du, Feng Zhu, Fu Chen, Zhe Wang, Tingjun Hou*, Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of Enhanced Sampling and Variable Dielectric Model on Protein-Protein Interactions, Physical Chemistry Chemical Physics, 2019, 21, 18958-18969.
3. Gaoqi Weng, Ercheng Wang, Fu Chen, Huiyong Sun, Zhe Wang, Tingjun Hou*, Assessing the performance of the MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes, Physical Chemistry Chemical Physics, 2019, 21, 10135-10145.
4. Fu Chen, Huiyong Sun, Junmei Wang, Feng Zhu, Hui Liu, Zhe Wang, Tailong Lei, Youyong Li, Tingjun Hou*, Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes, RNA, 2018, 24, 1183-1194.
5. Huiyong Sun, Lili Duan, Fu Chen, Hui Liu, Zhe Wang, Peichen Pan, Feng Zhu, John Z. H. Zhang, Tingjun Hou*, Assessing the performance of MM/PBSA and MM/GBSA methods. 7. Entropy effects on the performance of end-point binding free energy calculation approaches, Physical Chemistry Chemical Physics, 2018, 20, 14450-14460.
6. Fu Chen, Hui Liu, Huiyong Sun, Peichen Pan, Youyong Li, Dan Li, Tingjing Hou*, Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking, Physical Chemistry Chemical Physics, 2016, 18, 22129-22139.
7. Huiyong Sun, Youyong Li, Mingyun Shen, Sheng Tian, Lei Xu, Peichen Pan, Yan Guan, Tingjun Hou*, Assessing the Performance of MM/PBSA and MM/GBSA Methods. 5. Improved Docking Performance by Using High Solute Dielectric Constant MM/GBSA and MM/PBSA Rescoring, Physical Chemistry Chemical Physics, 2014, 16, 22035-22045.
8. Huiyong Sun, Youyong Li, Sheng Tian, Lei Xu, Tingjun Hou*, Assessing the Performance of MM/PBSA and MM/GBSA Methods. 4. Accuracies of MM/PBSA and MM/GBSA Methodologies Evaluated by Various Simulation Protocols using PDBbind Data Set, Physical Chemistry Chemical Physics, 2014, 16, 16719-16729.
9. Lei Xu, Huiyong Sun, Youyong Li, Junmei Wang, Tingjun Hou*, Assessing the Performance of MM/PBSA and MM/GBSA Methods. 3. The Impact of Force Fields and Ligand Charge Models, Journal of Physical Chemistry B, 2013, 117, 8408-8421.
10. Tingjun Hou*, Junmei Wang, Youyong Li, Wei Wang*, Assessing the performance of the Molecular Mechanics/Poisson Boltzmann Surface Area and Molecular Mechanics/ Generalized Born Surface Area methods. II. The accuracy of ranking poses generated from docking, Journal of Computational Chemistry, 2011, 32, 866-877.
11. Tingjun Hou*, Junmei Wang, Youyong Li, Wei Wang*, Assessing the performance of the MM/PBSA and MM/GBSA methods: I. The accuracy of binding free energy calculations based on molecular dynamics simulations, Journal of Chemical Information and Modeling, 2011, 51, 69-82.
12. Ercheng Wang, Weitao Fu, Dejun Jiang, Huiyong Sun, Junmei Wang, Xujun Zhang, Gaoqi Weng, Hui Liu, Peng Tao, and Tingjun Hou*, VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein–Ligand Binding Free Energy Calculations, Journal of Chemical Information and Modeling, 2021, 61, 2844-2856.
13. Ercheng Wang, Hui Liu, Junmei Wang, Gaoqi Weng, Huiyong Sun, Zhe Wang, Yu Kang, Tingjun Hou*, Development and Evaluation of MM/GBSA Based on a Variable Dielectric GB Model for Predicting Protein-Ligand Binding Affinities, Journal of Chemical Information and Modeling, 2020, 60, 5353-5365.
14. Gaoqi Weng, Ercheng Wang, Zhe Wang, Hui Liu, Feng Zhu, Dan Li, Tingjun Hou*, HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA, Nucleic Acids Research, 2019, 47, W322-W330.
15. Hui Liu, Tingjun Hou*, CaFE: a tool for binding affinity prediction using end-point free energy methods, Bioinformatics, 2016, 32, 2216-2218.
16. Zhe Wang, Xuwen Wang, Youyong Li, Tailong Lei, Ercheng Wang, Dan Li, Yu Kang, Feng Zhu, Tingjun Hou*, farPPI: a webserver for accurate prediction of protein-ligand binding structures for small-molecule PPI inhibitors by MM/PB(GB)SA methods, Bioinformatics, 2019, 35, 1777-1779.
17. Tingjun Hou*, Nan Li, Youyong Li, Wei Wang*, Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models, Journal of Proteome Research, 2012, 11, 2982-2995.
18. Zheng Xu#, Tingjun Hou# (co-first author), Nan Li, Yang Xu, Wei Wang, Proteome-wide detection of Abl1 SH3 binding peptides by integrating computational prediction and peptide microarray, Molecular & Cellular Proteomics, 2012, 11, O111.010389.
19. Sheng Tian, Huiyong Sun, Youyong Li, Dan Li, Tingjun Hou*, Development and evaluation of an integrated virtual screening strategy by combining molecular docking and pharmacophore searching based on multiple protein structures, Journal of Chemical Information and Modeling, 2013, 53, 2743-2756.
20. Xiaotian Kong, Huiyong Sun, Peichen Pan, Feng Zhu, Shan Chang, Lei Xu, Youyong Li, Tingjun Hou*, Importance of protein flexibility in molecular recognition: a case study on Type-I1/2 inhibitors of ALK, Physical Chemistry Chemical Physics, 2018, 20, 4851-4863.
Identification and optimization of lead compound are inalienable parts of new drug discovery pipelines. With the purpose of providing hits with novel chemical scaffolds, computational virtual screening (VS) has exhibited a remarkably increasing influence on the early phase of drug discovery. We tried to discover leads for important drug targets by combining in silico, in vitro and in vivo assays. Our strength rests in a full spectrum of lead discovery, chemistry optimization, ADMET evaluation and chemistry/biology solution, which can expedite drug discovery and development programs at fairly lower costs. Our efforts involve a wide range of biological targets, such as AR, GR, ALK, ROCK, PI3K, MIF, ALK, Tie-2 and A2A [1~15]. For example, a new class of anti-drug ALK "bridged" small molecules were designed and synthesized using various CADD methods such as MIEC-SVM prediction model, molecular docking, and binding free energy calculation, and they can simultaneously target the ATP active site and the adjacent hydrophobic allosteric pocket. This class of inhibitors exhibit excellent biological activity at the molecular and cellular levels, and and the best candidate is currently the most active type I1/2 ALK inhibitor reported in vitro (001-017: IC50=0.27 nM), which is more active than the marketed drugs crizotinib and ceritinib. Among them, 001-017 also shows strong inhibitory activity and high kinase selection against a variety of ALK-resistant mutants .
Figure 4. Discovery pipeline of novel ALK inhibitors.
1. Rui Shi, Peichen Pan, Rui Lv, Chongqing Ma, Enhui Wu, Ruochen Guo, Zhihao Zhao, Hexing Song, Joe Zhou, Yang Liu, Guoqiang Xu, Tingjun Hou*, Zhenhui Kang*, Jian Liu*, High-throughput glycolytic inhibitor discovery targeting glioblastoma by graphite dots-assisted LDI mass spectrometry, Science Advances, 2022, 8, eabl4923.
2. Xin Chai, Huiyong Sun, Wenfang Zhou, Changwei Chen, Luhu Shan, Yuhui Yang, Junzhao He, Jinping Pang, Liu Yang, Xinyue Wang, Sunliang Cui, Yaqin Fu, Xiaohong Xu, Lei Xu, Xiaojun Yao, Dan Li*, Tingjun Hou*, Discovery of N-(4-(Benzyloxy)-phenyl)-sulfonamide Derivatives as novel antagonists of the human androgen receptor targeting the activation function 2, Journal of Medicinal Chemistry, 2022, 65, 2507-2521.
3. Jinping Pang, Chao Shen, Wenfang Zhou, Yunxia Wang, Luhu Shan, Xin Chai, Ying Shao, Xueping Hu, Feng Zhu, Danyan Zhu, Li Xiao, Lei Xu, Xiaohong Xu, Dan Li*, Tingjun Hou*, Discovery of novel antagonists targeting the DNA binding domain of androgen receptor by integrated docking-based virtual screening and bioassays, Acta Pharmacologica Sinica, 2022, 43, 229-239.
4. Weitao Fu, Minkui Zhang, Jianing Liao, Qing Tang, Yixuan Lei, Zhou Gong, Luhu Shan, Mojie Duan, Xin Chai, Jinping Pang, Chun Tang, Xuwen Wang, Xiaohong Xu, Dan Li*, Rong Sheng*, Tingjun Hou*, Discovery of a novel androgen receptor antagonist manifesting evidence to disrupt the dimerization of the ligand-binding domain via attenuating the hydrogen-bonding network, Journal of Medicinal Chemistry, 2021, 64, 17221-17238.
5. Qin Tang, Weitao Fu, Minkui Zhang, Ercheng Wang, Lvhu Shan, Xin Chai, Jinping Pang, Xuwen Wang, Xiaohong Xu, Lei Xu, Dan Li*, Rong Sheng*, Tingjun Hou*, Novel androgen receptor antagonist identified by structure-based virtual screening, structural optimization, and biological evaluation, European Journal of Medicinal Chemistry, 2020, 192, 112156.
6. Wenfang Zhou, Mojie Duan, Weitao Fu, Jinping Pang, Qin Tang, Huiyong Sun, Lei Xu, Shan Chang, Dan Li*, Tingjun Hou*, Discovery of Novel Androgen Receptor Ligands by Structure-based Virtual Screening and Bioassays, Genomics, Proteomics & Bioinformatics, 2019, 16, 416-427.
7. Xueping Hu, Jinping Pang, Jintu Zhang, Chao Shen, Xin Chai, Ercheng Wang, Haiyi Chen, Xuwen Wang, Mojie Duan, Weitao Fu, Lei Xu, Yu Kang, Dan Li*, Hongguang Xia*, Tingjun Hou*, Discovery of novel GR ligands towards druggable GR antagonist conformations identified by MD Simulations and Markov state model analysis, Advanced Science, 2022, 9, 2102435.
8. Weitao Fu, Ercheng Wang, Di Ke, Hao Yang, Lingfeng Chen, Jingjing Shao, Xueping Hu, Lei Xu, Na Liu*, Tingjun Hou*, Discovery of a novel Fusarium Graminearum Mitogen-activated Protein Kinase (FgGpmk1) inhibitor for the treatment of fusarium head blight, Journal of Medicinal Chemistry, 2021, 64, 13841-13852.
9. Yuan Wang, ZheWang, Jiacheng Liu, Yunwen Wang, Rui Wu, Rong Sheng*, Tingjun Hou*, Discovery of novel HBV capsid assembly modulators by structure-based virtual screening and bioassays, Bioorganic & Medicinal Chemistry, 2021, 36, 116096.
10. Yangshun Tang, Bo Feng, Yi Wang, Huiyong Sun, Yi You, Jie Yu, Bing Chen, Cengli Xu, Yeping Ruan, Sunliang Cui, Gang Hu, Tingjun Hou*, Zhong Chen*, Structure‐based discovery of caspase‐1 inhibitor with therapeutic potential for febrile seizures and later enhanced epileptogenic susceptibility, British Journal of Pharmacology, 2020, 17, 3519-3534.
11. Xiaotian Kong, Peichen Pan, Huiyong Sun, Hongguang Xia, Xuwen Wang, Youyong Li, Tingjun Hou*, Drug discovery targeting anaplastic lymphoma kinase (ALK), Journal of Medicinal Chemistry, 2019, 62, 10927-10954.
12. Peichen Pan, Huidong Yu, Qinglan Liu, Xiaotian Kong, Hu Chen, Jiean Chen, Qi Liu, Dan Li, Yu Kang, Huiyong Sun, Wenfang Zhou, Sheng Tian, Sunliang Cui, Feng Zhu, Youyong Li, Yong Huang*, Tingjun Hou*, Combating drug-resistant mutants of ALK with potent and selective Type-I1/2 inhibitors by stabilizing unique DFG-shifted loop conformation, ACS Central Science, 2017, 3, 1208-1220.
13. Peichen Pan, Jiean Chen, Xijian Li, Miyang Li, Huidong Yu, Jean J Zhao, Jing Ni, Xuwen Wang, Huiyong Sun, Sheng Tian, Feng Zhu, Feng Liu, Yong Huang*, Tingjun Hou*, Structure-based Drug Design and Identification of H2O-soluble and Low Toxic Hexacyclic Camptothecin Derivatives with Improved Efficacy in both Cancer and Lethal Inflammation Models In Vivo, Journal of Medicinal Chemistry, 2018, 61,8613–8624.
14. Lei Xu, Yu Zhang, Longtai Zheng, Chunhua Qiao,Youyong Li, Dan Li, Xuechu Zhen*, Tingjun Hou*, Discovery of novel inhibitors targeting macrophage migration inhibitory factor via structure-based virtual screening and bioassays, Journal of Medicinal Chemistry, 2014, 57, 3737-3745.
15. Jingyu Zhu, Kan Li, Li Yu, Yun Chen, Yanfei Cai, Jian Jin*, Tingjun Hou*, Targeting phosphatidylinositol 3‐kinase gamma (PI3Kγ): Discovery and development of its selective inhibitors, Medicinal Research Reviews, 2021, 43, 1599-1621.
Conventional methods in structural biology (X-ray and NMR) are useful to provide static snapshots of protein or protein/ligand conformations, but they are not fine enough to describe the dynamic processes involved in target-ligand recognition. In this context, atomistic molecular dynamics (MD) simulations (traditional MD and enhanced sampling MD), served as a “computational microscope”, may complement such experimental techniques by providing mechanical and thermodynamic information at the atomic level. In our group, conventional and advanced all-atom MD simulations, such as metadynamics, umbrella sampling (US), adaptive biasing force (ABF), etc., were employed to provide clues to understand the complexity in target-ligand recognition. We are especially interested in understanding the mechanisms of drug resistance, deciphering the working mechanisms of ABC transporters and predicting drug-target residence time by characterizing the free energy surfaces [1~14]. For example, based on the free energy surfaces through advanced MD techniques, it was revealed that the more rigid P-loop region in the G2032R-mutated ROS1 was primarily responsible for the crizotinib resistance, which impaired the binding of crizotinib directly and shortened the residence time induced by the flattened free energy surface (Figure 5) .
Figure 5. Free energy surface of crizotinib separated from G2032R-ROS1
Recently, we performed long time unbiased MD simulations and enhanced sampling MD simulations to explore the ligand binding domain of AR (AR-LBD) in complex with various ligands, and further constructed their free energy profiles by metadynamics simulations, which revealed the transition process between the antagonistic form and agonistic form of AR-LBD . Interestingly, many intermediate states characterized by the free energy profile of the antagonistic form of AR-LBD could provide potential targets for the design of efficient drugs to inhibit the activity of ARs (Figure 6).
Figure 6. Free energy profiles of AR-LBD in complex with agonist or antagonist.
1. Haiyi Chen, Rui Zhou, Jinping Pang, Yue Guo, Jiawen Chen, Yu Kang*, Mojie Duan*, Tingjun Hou*, Molecular view on the dissociation pathways and transactivation regulation mechanism of nonsteroidal GR ligands, Journal of Chemical Information and Modeling, 2022, in press.
2. Haiyi Chen, Yu Kang, Mojie Duan*, Tingjun Hou*, Regulation mechanism for the binding between the SARS-CoV-2 spike protein and host angiotensin-converting enzyme II, The Journal of Physical Chemistry Letters, 2021, 12, 6252-6261.
3. Yongchang Xu, Haiyi Chen, Huimin Zhang, Saif Ullah, Tingjun Hou*, Youjun Feng*, The MCR-3 inside linker appears as a facilitator of colistin resistance, Cell Reports, 2021, 35, 109135.
4. Ye Jin, Mojie Duan, Xuwen Wang, Xiaotian Kong, Wenfang Zhou, Huiyong Sun, Hui Liu, Dan Li, Huidong Yu, Youyong Li*, Tingjun Hou*, Communication between the ligand-binding pocket (LBP) and the activation function-2 (AF2) domain of androgen receptor revealed by molecular dynamics simulations, Journal of Chemical Information and Modeling, 2019, 59, 842-857.
5. Na Liu, Wenfang Zhou, Yue Guo, Junmei Wang, Weitao Fu, Huiyong Sun, Dan Li, Mojie Duan*, Tingjun Hou*, Molecular Dynamics Simulations Revealed the Regulation of Ligands to the Interactions between Androgen Receptor and Its Coactivator, Journal of Chemical Information and Modeling, 2018, 58, 1652-1661.
6. Huiyong Sun, Youyong Li, Mingyun Shen, Dan Li, Yu Kang. Tingjun Hou*, Characterizing drug-target residence time with metadynamics: how to achieve dissociation rate efficiently without losing accuracy against much time-consuming approaches, Journal of Chemical Information and Modeling, 2017, 57, 1895-1906.
7. Mojie Duan, Na Liu, Wenfang Zhou, Dan Li, Minghui Yang*, Tingjun Hou*, Structural diversity of ligand-binding androgen receptors revealed by microsecond long molecular dynamics simulations and enhanced sampling, Journal of Chemical Theory and Computation, 2016, 12, 4611-4619.
8. Huiyong Sun, Pengcheng Chen, Dan Li, Youyong Li, Tingjun Hou*, Directly-binding rather than induced-fit dominated binding affinity difference in (S) and (R)-crizotinib bound MTH1, Journal of Chemical Theory and Computation, 2016, 12, 851-860.
9. Hui Liu, Dan Li, Youyong Li, Tingjun Hou*, Atomistic molecular dynamics simulations of ATP-binding cassette transporters, WIREs Computational Molecular Science, 2016, 6, 255-265.
10. Nan Li, Richard I. Ainsworth, Bo Ding, Tingjun Hou*, and Wei Wang*, Using Hierarchical Virtual Screening to Combat Drug Resistance of the HIV-1 Protease, Journal of Chemical Information and Modeling, 2015, 55, 1400-1412.
11. Huiyong Sun, Youyong Li, Sheng Tian, Junmei Wang, Tingjun Hou*, P-loop Conformation Governed Crizotinib Resistance in G2032R-mutated ROS1 Tyrosine Kinase: Clues from Free Energy Landscape, PLoS Computational Biology, 2014, 10, e1003729.
12. Yan Guan, Huiyong Sun, Youyong Li, Peichen Pan, Dan Li, Tingjun Hou*, The competitive binding between inhibitors and substrates of HCV NS3/4A protease: a general mechanism of drug resistance, Antiviral Research, 2014, 103, 60-70.
13. Huiyong Sun, Youyong Li*, Dan Li, Tingjun Hou*, Insight into Crizotinib Resistance Mechanisms Caused by Three Mutations in ALK Tyrosine Kinase using Free Energy Calculation Approaches, Journal of Chemical Information and Modeling, 2013, 53, 2376-2389.
14. Peichen Pan, Youyong Li, Huidong Yu, Tingjun Hou*, Molecular principle of topotecan resistance by topoisomerase I mutations through molecular modeling approaches, Journal of Chemical Information and Modeling, 2013, 53, 997-1006.
15. Jing Zhang#, Tingjun Hou# (Co-first author), Wei Wang, Jun S. Liu, Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance, Proceedings of the National Academy of Sciences, 2010, 107, 1321-1326.