Tingjun Hou

Tingjun Hou
Zhejiang University | ZJU · College of Pharmaceutical Sciences

PhD

About

469
Publications
99,468
Reads
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19,598
Citations
Introduction
Prof. Hou's group primarily focuses on the development and application of state-of-art computational and theoretical techniques to investigate the structures, functions, and dynamics of important drug targets and design potential drug candidates using computational approaches and chemical/biological experiments. http://cadd.zju.edu.cn
Additional affiliations
March 2013 - present
Zhejiang University
Position
  • Professor (Full)
October 2009 - March 2013
Soochow University (PRC)
Position
  • Professor (Full)
September 2004 - February 2009
University of San Diego
Position
  • PostDoc Position

Publications

Publications (469)
Article
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Acute myeloid leukaemia (AML) is one of the most common types of haematopoietic malignancy. Ribonucleotide reductase (RNR) is a key enzyme required for DNA synthesis and cell proliferation, and its small subunit RRM2 plays a key role for the enzymatic activity. We predicted monobenzone (MB) as a potential RRM2 target compound based on the crystal s...
Article
The androgen receptor (AR) antagonists are efficient therapeutics for the treatment of prostate cancer (PCa). All the approved AR antagonists to date are targeted to the ligand-binding pocket (LBP) of AR and have suffered from various drug resistances, whereas AR antagonist targeting non-LBP site of AR is conceived as a promising strategy. Through...
Article
Many deep learning (DL)-based molecular generative models have been proposed to design novel molecules. These models may perform well on benchmarks, but they usually do not take real-world constraints into account, such as available training data set, synthetic accessibility, and scaffold diversity in drug discovery. In this study, a new algorithm,...
Article
The acid–base dissociation constant (pKa) is a fundamental property influencing many ADMET properties of small molecules. However, rapid and accurate pKa prediction remains a great challenge. In this review, we outline the current advances in machine-learning-based QSAR models for pKa prediction, including descriptor-based and graph-based approache...
Article
The mechanism of transcriptional activation/repression of the nuclear receptors (NRs) involves two main conformations of the NR protein, namely, the active (agonistic) and inactive (antagonistic) conformations. Binding of agonists or antagonists to the ligand-binding pocket (LBP) of NRs can regulate the downstream signaling pathways with different...
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Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is st...
Article
Motivation Automatic recognition of chemical structures from molecular images provides an important avenue for the rediscovery of chemicals. Traditional rule-based approaches that rely on expert knowledge and fail to consider all the stylistic variations of molecular images usually suffer from cumbersome recognition processes and low generalization...
Article
The past few years have witnessed enormous progress toward applying machine learning approaches to the development of protein-ligand scoring functions. However, the robust performance and wide applicability of scoring functions remain a big challenge for increasing the success rate of docking-based virtual screening. Herein, a novel scoring functio...
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DNA methyltransferases (DNMTs) are important epigenetic regulatory enzymes involved in gene expression corresponding to many diseases including cancer. As one of the major enzymatically active mammalian DNMTs, DNMT3A has been regarded as an attractive target for the treatment of cancer particularly in hematological malignancy. Discovery of promisin...
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Acquired resistance to cetuximab in colorectal cancers is partially mediated by the acquisition of mutations located in the cetuximab epitope in the epidermal growth factor receptor (EGFR) ectodomain and hinders the clinical application of cetuximab. We develop a structure-guided and phage-assisted evolution approach for cetuximab evolution to reve...
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Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindere...
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Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates synthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES is adopted for molecule representations. However, the general-purpose SMILES...
Preprint
Acid-base dissociation constant (pKa) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pKa prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pKa (Multi-Fidelity modeling with Subgraph Pooling for pKa pred...
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Creating a wide range of new compounds that not only have ideal pharmacological properties but also easily pass long-term toxicity evaluation is still a challenging task in current drug discovery. In this study, we developed a conditional generative model by combining a semisupervised variational autoencoder (SSVAE) with an MGA toxicity predictor....
Article
Deep learning (DL)-based de novo molecular design has recently gained considerable traction. Many DL-based generative models have been successfully developed to design novel molecules, but most of them are ligand-centric and the role of the 3D geometries of target binding pockets in molecular generation has not been well-exploited. Here, we propose...
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DNA methyltransferase 3A (DNMT3A) has been regarded as a potential epigenetic target for the development of cancer therapeutics. A number of DNMT3A inhibitors have been reported, but most of them do not have good potency, high selectivity and/or low cytotoxicity. It has been suggested that a non-conserved region around the target recognition domain...
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Accurate estimation of the synthetic accessibility of small molecules is needed in many phases of drug discovery. Several expert-crafted scoring methods and descriptor-based quantitative structure-activity relationship (QSAR) models have been developed for synthetic accessibility assessment, but their practical applications in drug discovery are st...
Article
Development of accurate machine-learning-based scoring functions (MLSFs) for structure-based virtual screening against a given target requires a large unbiased dataset with structurally diverse actives and decoys. However, most datasets for the development of MLSFs were designed for traditional SFs and may suffer from hidden biases and data insuffi...
Article
Predicting the native or near-native binding pose of a small molecule within a protein binding pocket is an extremely important task in structure-based drug design, especially in the hit-to-lead and lead optimization phases. In this study, fastDRH, a free and open accessed web server, was developed to predict and analyze protein-ligand complex stru...
Preprint
Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale inform...
Article
Progressive ischemic stroke (PIS) is featured by progressive neurological dysfunction after ischemia. Ischemia-evoked neuroinflammation is implicated in the progressive brain injury after cerebral ischemia, while Caspase-1, an active component of inflammasome, exaggerates ischemic brain injury. Current Caspase-1 inhibitors are inadequate in safety...
Article
Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development, such as lead discovery, drug repurposing and elucidation of potential drug side effects. Therefore, a variety of machine learning-based models have been developed to predict these interactions. In this study, a...
Article
Molecular property prediction models based on machine learning algorithms have become important tools to triage unpromising lead molecules in the early stages of drug discovery. Compared with the mainstream descriptor- and graph-based methods for molecular property predictions, SMILES-based methods can directly extract molecular features from SMILE...
Article
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Drug–drug interaction (DDI) often causes serious adverse reactions and thus results in inestimable economic and social loss. Currently, comprehensive DDI evaluation has become a major challenge in pharmaceutical research due to the time-consuming and costly process of the experimental assessment and it is of high necessity to develop effective in s...
Article
Drug design targeting protein-protein interactions (PPIs) associated with the development of diseases has been one of the most important therapeutic strategies. Besides interrupting the PPIs with PPI inhibitors/blockers, increasing evidence shows that stabilizing the interaction between two interacting proteins may also benefit the therapy, such as...
Article
Glucocorticoids (GCs) are the most commonly used anti-inflammatory drugs. However, their excellent therapeutic effects are often accompanied by undesirable side effects. To discover selective glucocorticoid receptor modulators (SGRMs) that preferentially induce transrepression with little or no transactivation activity, a structure-based virtual sc...
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Ferroptosis is an iron-dependent, non-apoptotic form of regulated cell death characterized by an accumulation of lipid- derived reactive oxygen species (ROS). The small-molecule compound erastin induces ferroptosis via inhibiting the cystine-glutamate antiporter system xc–, which consists of two subunits, namely the light chain xCT and the heavy ch...
Preprint
Full-text available
Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to discover precursor molecules that can be used to synthesize a target molecule. A popular paradigm of existing computational retrosynthesis methods formulate retrosynthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES r...
Article
Structural information for chemical compounds is often described by pictorial images in most scientific documents, which cannot be easily understood and manipulated by computers. This dilemma makes optical chemical structure recognition (OCSR) an essential tool for automatically mining knowledge from an enormous amount of literature. However, exist...
Article
Malignant tumors will become vulnerable if their uncontrolled biosynthesis and energy consumption engaged in metabolic reprogramming can be cut off. Here, we report finding a glycolytic inhibitor targeting glioblastoma with graphite dots-assisted laser desorption/ionization mass spectrometry as an integrated drug screening and pharmacokinetic platf...
Article
De novo drug design is the process of generating novel lead compounds with desirable pharmacological and physiochemical properties. The application of deep learning (DL) in de novo drug design has become a hot topic, and many DL-based approaches have been developed for molecular generation tasks. Generally, these approaches were developed as per fo...
Article
Synthetic glucocorticoids (GCs) have been widely used in the treatment of a broad range of inflammatory diseases, but their clinic use is limited by undesired side effects such as metabolic disorders, osteoporosis, skin and muscle atrophies, mood disorders and hypothalamic-pituitary-adrenal (HPA) axis suppression. Selective glucocorticoid receptor...
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Binding of different ligands to glucocorticoid receptor (GR) may induce different conformational changes and even trigger completely opposite biological functions. To understand the allosteric communication within the GR ligand binding domain, the folding pathway of helix 12 (H12) induced by the binding of the agonist dexamethasone (DEX), antagonis...
Article
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Accurate prediction of atomic partial charges with high-level quantum mechanics (QM) methods suffers from high computational cost. Numerous feature-engineered machine learning (ML)-based predictors with favorable computability and reliability have been developed as alternatives. However, extensive expertise effort was needed for feature engineering...
Article
Recently, deep learning (DL)-based de novo drug design represents a new trend in pharmaceutical research, and numerous DL-based methods have been developed for the generation of novel compounds with desired properties. However, a comprehensive understanding of the advantages and disadvantages of these methods is still lacking. In this study, the pe...
Article
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Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the c...
Article
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In the process of drug discovery, the optimization of lead compounds has always been a challenge faced by pharmaceutical chemists. Matched molecular pair analysis (MMPA), a promising tool to efficiently extract and summarize the relationship between structural transformation and property change, is suitable for local structural optimization tasks....
Article
Decaprenylphosphoryl-β-D-ribose oxidase (DprE1) plays important roles in the biosynthesis of mycobacterium cell wall. DprE1 inhibitors have shown great potentials in the development of new regimens for tuberculosis (TB) treatment. In this study, an integrated molecular modeling strategy, which combined computational bioactivity fingerprints and str...
Article
Full-text available
Structure-based drug design depends on the detailed knowledge of the three-dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of ligand-binding poses is still a major challenge for molecular docking due to deficiency of scoring functions (SFs) and ignorance of protein flexibility upon ligand binding. In this st...
Article
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Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Despite the continuous information accumulation of clinically significant DDIs, there are few open-access knowledge systems dedicated to the curation of DDI associations. To facilitate the clinicians to screen for dang...
Article
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Machine learning-based generative models can generate novel molecules with desirable physiochemical and pharmacological properties from scratch. Many excellent generative models have been proposed, but multi-objective optimizations in molecular generative tasks are still quite challenging for most existing models. Here we proposed the multi-constra...
Article
Microglial overactivation-mediated neuroinflammation contributes greatly to the pathogenesis of neurodegenerative diseases, such as Parkinson’s disease. Macrophage migration inhibitory factor (MIF) is a pleiotropic proinflammatory cytokine that is involved in the pathophysiology of various inflammatory diseases by inducing various proinflammatory c...
Article
There are still huge challenges from clinical real-world data to accurate targets and critical quality attributes (CQAs) for effective treatment of allergic rhinitis (AR). Here, we present a novel integrated strategy that biosensors and intelligent algorithms were used to angle AR targets and CQAs from clinical real-world. Firstly, bagging and boos...
Article
Tuberculosis (TB), an airborne infectious disease mainly caused by Mycobacterium tuberculosis (Mtb), remains a leading cause of human morbidity and mortality worldwide. Given the alarming rise of resistance to anti-TB drugs and latent TB infection (LTBI), new targets and novel bioactive compounds are urgently needed for the treatment of this diseas...
Article
Melanocortin-4 receptor (MC4R) plays a central role in the regulation of energy homeostasis. Its high sequence similarity to other MC receptor family members, low agonist selectivity and the lack of structural information concerning MC4R-specific activation have hampered the development of MC4R-seletive therapeutics to treat obesity. Here, we repor...
Article
Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert knowledge to devise useful features, which could be costly and sometimes biased. The emerging deep...
Article
The predictive performance of classical scoring functions (SFs) seems to have reached a plateau. Currently, SFs relying on sophisticated machine learning techniques have shown great potential in binding affinity prediction and virtual screening. As one of the most indispensable components in the workflow of training a machine learning scoring funct...
Article
Macrophage migration inhibitory factor (MIF) is a pluripotent pro-inflammatory cytokine and is related to acute and chronic inflammatory responses, immune disorders, tumors, and other diseases. In this study, an integrated virtual screening strategy and bioassays were used to search for potent MIF inhibitors. Twelve compounds with better bioactivit...
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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is mainly mediated through the interaction between the spike protein (S-pro) of the virus and the host angiotensin-converting enzyme II (ACE2). The attachment of heparan sulfate (HS) to S-pro is necessary for its binding to ACE2. In this study, the binding process of the recepto...
Article
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Protein neddylation is catalyzed by a three-enzyme cascade, namely an E1 NEDD8-activating enzyme (NAE), one of two E2 NEDD8 conjugation enzymes and one of several E3 NEDD8 ligases. The physiological substrates of neddylation are the family members of cullin, the scaffold component of cullin RING ligases (CRLs). Currently, a potent E1 inhibitor, MLN...
Article
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Chaperone-mediated autophagy (CMA) is a lysosome-dependent selective degradation pathway implicated in the pathogenesis of cancer and neurodegenerative diseases. However, the mechanisms that regulate CMA are not fully understood. Here, using unbiased drug screening approaches, we discover Metformin, a drug that is commonly the first medication pres...
Article
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G-protein-coupled receptors (GPCRs) have central roles in intercellular communication1,2. Structural studies have revealed how GPCRs can activate G proteins. However, whether this mechanism is conserved among all classes of GPCR remains unknown. Here we report the structure of the class-C heterodimeric GABAB receptor, which is activated by the inhi...
Preprint
Full-text available
Melanocortin-4 receptor (MC4R) plays a central role in the regulation of energy homeostasis. Its high sequence similarity to other MC receptor family members, low agonist selectivity and the lack of structural information concerning receptor activation have hampered the development of MC4R-seletive therapeutics to treat obesity. Here, we report fou...
Article
Full-text available
Hepatocellular carcinoma (HCC), one of the most deadly diseases all around the world. HBV infection is a causative factor of HCC and closely associated with HCC development. Ribonucleotide reductase (RR) is a key enzyme for cellular DNA synthesis and RR small subunit M2 (RRM2) is highly upregulated in HCC with poor survival rates. We have previousl...
Article
High-level quantum mechanics (QM) methods are no doubt the most reliable approaches for the prediction of atomic charges, but it usually needs very large computational resources, which apparently hinders the use of high-quality atomic charges in large-scale molecular modeling, such as high-throughput virtual screening. To solve this problem, severa...