Invited Speakers

 

Asst. Prof. Faez Iqbal Khan
Xi'an Jiaotong-Liverpool University, China

 

Dr. Faez Iqbal Khan is currently serving as an Assistant Professor within the Department of Biological Sciences at Xi'an Jiaotong-Liverpool University. He holds a Ph.D. degree in Computational Chemistry (Bioinformatics) from Durban University of Technology, South Africa. Dr. Khan has obtained Bachelor's and Master's degrees in Biomedical Science and Bioinformatics, respectively. Throughout his career, Dr. Khan has conducted research and teaching across esteemed institutions such as Rhodes University (South Africa), South China University of Technology, and the University of Electronic Science and Technology of China. His main areas of research focus on Protein engineering, Protein folding, drug design, and Protein dynamics. Dr. Khan established wide-ranging collaborations with BRICS countries and mentored several postgraduate students. He has authored over 75 publications in international peer-reviewed journals, which are well cited.

 

Speech Title: "Computational Insights into SARS-CoV-2 Inhibition: Investigating Drug Derivatives and Natural Compounds"

 

Abstract: In this study, computational approaches were employed to identify natural compounds against SARS-CoV-2. Screening of psilocybin-mushroom metabolites revealed inhibitory effects on SARS-CoV-2 Mprotease and human interleukin-6 receptors, suggesting their potential as therapeutic agents. The binding of psilacetin to the Mprotease of SARS-CoV-2 and human interleukin-6 receptors alters the structural dynamics and Gibbs free energy patterns of proteins. These results suggest that psilocybin-mushroom metabolites could serve as viable potential chemotherapeutic agents for SARS-CoV-2. Additionally, text mining and screening identified fangchinoline and versicolactone C as natural compounds with strong binding affinity to multiple structural proteins of SARS-CoV-2, including the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein. Experimental validation of these findings is recommended for further exploration.  

 

Prof. Feng Zhu
Zhejiang University, China

 

Dr. Feng Zhu is Distinguished Professor of Zhejiang University, China and Tenured Full Professor of Zhejiang University, China. He is Associate Editor of Journal of Chemical Information and Modeling, President of Intelligent Pharmaceutical Sciences Society and Bioinformatics Society of Zhejiang. He is the Secretary-General of Computational Chemistry and Chinese Chemical Society. He has published more than 170 research papers in reputable journals such as Nature Biotechnology, Nature Protocols, Nature Machine Intelligence, Nucleic Acids Research, PNAS, and Nature Reviews Drug Discovery. For more information, please visit the official website of his lab at: https://idrblab.org/Peoples-PI.php.

 

Speech Title: "Artificial Intelligence (AI)-Aided Drug Target Discovery"

 

Abstract: Drug development is characterized by high difficulty, high risk and long development cycles, and the success rate of new drug is on a decline. In recent years, the development of artificial intelligence has brought new technologies to drug development. By empowering drug target discovery and compound screening through machine learning and deep learning, the efficiency of new drug development is expected to be enhanced. Based on artificial intelligence and OMIC (proteomics and metabolomics) technologies, we conduct systematical exploration on the druggability and system profile of therapeutic targets, develop novel methods and online tools for target discovery, and further study the mechanism underlying the interaction between drugs and their targets.  

 

Assoc. Prof. Francesco Zonta
Xi’an Jiaotong Liverpool University, China

 

Francesco Zonta obtained his Ph.D. in Physics in Padova University (Italy) in 2007. He initiated his career in Computational Biology as Post-Doc and young researcher at the Venetian Institute of Molecular Medicine, working on models of connexin hemichannel permeation. In 2015 he joined the Shanghai Institute for Advanced Immunochemical Studies (ShanghaiTech University) as director of the Bioinformatics platform and Co-PI of the laboratory of Computational Biology, working on computer simulations of antibody-antigen interactions. From 2023 he is Associate Professor in the Department of Biological Sciences of Xi’an Jiaotong Liverpool University. He authored more than 50 articles in peer-reviewed international journals in the fields of computational biology, molecular biology, and statistical mechanics.

 

Speech Title: "In Silico Design of Therapeutic Antibodies"

 

Abstract: Antibodies have emerged as a prominent class of therapeutic proteins, owing to their exceptional binding affinity and specificity toward target molecules. However, designing therapeutic antibodies through computational methods remains a formidable challenge due to the vast sequence space and conformational complexity of these biomolecules. In this talk, we present an approach that combines Molecular Dynamics simulations with intelligent sequence sampling algorithms to explore the antibody sequence landscape and identify variants with improved biochemical properties. This methodology has been successfully applied to several important pharmaceutical targets, yielding promising results. Notably, we have engineered a high affinity nanomolar antagonist of the CXC chemokine receptor 2 (CXCR2), a key mediator in inflammatory disorders, by enhancing the binding affinity of an existing antibody candidate. Additionally, we have designed a potent antibody capable of blocking connexin hemichannels, which play critical roles in various pathological conditions, including ischemic injury and cancer.Our work demonstrates the potential of integrating advanced computational techniques with experimental validation to accelerate the discovery and optimization of therapeutic antibodies, paving the way for more effective and targeted treatments.  

 

Prof. Jianbo Pan
Chongqing Medical University, China

 

Dr. Jianbo Pan is a Professor of bioinformatics at Chongqing Medical University. He received a bachelor’s degree in Chemical biology (2009) and a Ph.D. in bioinformatics (2014) from Xiamen University, and conducted a 6-year postdoctoral research supervised by Dr. Jiang Qian and Dr. Hui Zhang at Johns Hopkins University School of Medicine. Since 2020, as a PI, he set up Intelligent Bioinformatics Research Group (InBiRG) in Chongqing Medical University. Dr. Pan's current research involves bioinformatics and multi-omics integration, especially in the field of pan-disease bioinformatics analysis. As first/corresponding author, he has published 25 peer-reviewed papers in reputable journals such as Cell, Nature Communications, Nucleic Acids Research, Cell Reports, and Briefings in bioinformatics.

 

Speech Title: "Accurate Prediction of the Maximum Recommended Daily Dose through Multi-feature Fusion, Cross-Validation Screening and Extreme Gradient Boosting"

 

Abstract: In the drug development process, approximately 30% of failures are attributed to drug safety issues. In particular, the first-in-human (FIH) trial of a new drug represents one of the highest safety risks, and initial dose selection is crucial for ensuring safety in clinical trials. With traditional dose estimation methods, which extrapolate data from animals to humans, catastrophic events have occurred during Phase I clinical trials due to interspecies differences in compound sensitivity and unknown molecular mechanisms. To address this issue, this study proposes a CrossFuse-extreme gradient boosting (XGBoost) method that can directly predict the maximum recommended daily dose of a compound based on existing human research data, providing a reference for FIH dose selection. This method not only integrates multiple features, including molecular representations, physicochemical properties and compound–protein interactions, but also improves feature selection based on cross-validation. The results demonstrate that the CrossFuse-XGBoost method not only improves prediction accuracy compared to that of existing local weighted methods [k-nearest neighbor (k-NN) and variable k-NN (v-NN)] but also solves the low prediction coverage issue of v-NN, achieving full coverage of the external validation set and enabling more reliable predictions. Furthermore, this study offers a high level of interpretability by identifying the importance of different features in model construction. The 241 features with the most significant impact on the maximum recommended daily dose were selected, providing references for optimizing the structure of new compounds and guiding experimental research.

 

 

 

Assoc. Prof. Shweta Gupta
Woxsen University, India

 

Dr. Shweta Gupta is Associate Professor at Woxsen University, India. She has 22 years of Experience of both in Multinational Companies Like Patni Computers, Tech Mahindra, Reliance Infocomm, and Colleges in and around Delhi – NCR. She is a Senior member of HKCBEES. She was awarded a Certificate of Merit for Outstanding academic performance and being among the top 0.1 percent of successful candidates of C.B.S.E. in XII standard. She was sent as a Senior Scientist under the International Travel Support Scheme by the Science and Engineering Research Board (SERB Department) was sent to ICBBT2015, Singapore for a Research Paper Presentation. She is doing her post-doc on “Early Prediction of Epilepsy using Genetic Algorithm and Ph.D. in Electronics and Communication" Neurostimulators used in Brain for “Treatment of Parkinson's Disease and Epilepsy using Bionics" and has done her Executive Global Business Management Programme from I.I.M. Lucknow and M.S. from Bits Pilani and B.E. in Electronics and Communication Engineering from Pune University. She is Editor in IGI Global's “Bio-Inspired Algorithms and Devices for Cognitive Diseases using Future Technologies" and Editor in Taylor and Francis’s “Cognitive Predictive Maintenance Tools in Brain Diseases: Design and Analysis".

 

Speech Title: "AI Machines for Treatment of Cognitive Diseases"

 

Abstract: With the advent of Artificial Intelligence in all facets of life, the advent of AI based Clinical psychologist robots that would talk to patients in their own mother tongue and counselling can be taken as and when required. Besides that, a psychiatrist can to maximum extent be replaced by an AI machine which can prescribe medicines and go into the remote areas where not even proper medical facilities are available. These AI based robotic clinical psychologists and psychiatrists can be implemented using generative AI or Azure AI using features such as Natural Language Processing (NLP) and speech processing and computer vision. These inventions are very useful for remote areas where ample medical facilities are not available. In parallel to that, Artificial Intelligence based ATM type medicine dispensing machines can be installed that can provide medicines immediately as a first aid before a patient can be taken to hospital. Thus, Artificial Intelligence based machines can work wonders in Cognitive treatment of different Cognitive Diseases which will be further discussed. So, the implementation of the same using AI and various Bioinformatics techniques and various AI techniques for detection of Cognitive Disease would be incorporated during the talk.  

 

Assoc. Prof. Weiwei Xue
Chongqing University, China

 

Dr. Weiwei Xue is an Associate Professor of Pharmaceutical Sciences at Chongqing University. He received a bachelor’s degree in Chemistry (2006) and a Ph.D. in Cheminformatics (2014) from Lanzhou University. He worked as a visiting scholar in the Institute for Protein Design (IPD) at the University of Washington (2018-2019). The research in Dr. Xue’s Lab is focused on constructing and maintaining disease- and therapeutic-related bioinformatics databases and tools, and developing artificial intelligence and molecular modeling methods to design innovative small molecules or protein binders against molecular targets of complex diseases, including psychiatric disorders, viral infection, and cancer. He has published more than 90 peer-reviewed papers in the area of bioinformatics and computational drug design (https://scholar.google.com/citations?user=nnY4O4QAAAAJ). He is also an editorial board member of Computers in Biology and Medicine.

 

Speech Title: "Identification of Cryptic Allosteric Sites on Human Serotonin Transporter for Novel Inhibitor Discovery"

 

Abstract: Serotonin transporter (SERT) plays a fundamental role in taking the synaptic cleft serotonin back to the presynaptic neuron. The discovery of allosteric SERT modulators represents the next-generation medication for psychiatric disorders such as depression. Here, based on the cryo-EM structures of ibogaine in complex with SERT in distinct conformations, the multiple functional structures of the transporter bound to serotonin, including outward-open (OOholo), outward-occluded (OCholo), and inward-open (IOholo and IOholo'), were carefully characterized by induced-fit docking Gaussian-accelerated molecular dynamics (IFD-GaMD) simulation and the free-energy landscape analysis. Further MM/GBSA binding free energy, per-residue contribution, and molecular interaction fingerprint calculations revealed the interaction variations of serotonin with SERT in functional structures, which confirmed the allostery of SERT during serotonin reuptake. Then, five unique cryptic allosteric sites, which are druggable and capable of targeting by small molecules, were identified on the characterized multistate structures. Moreover, with one of the potential druggable allosteric sites on SERT in OO conformation, virtual screening of approved drug database was processed, resulting in 8 compounds being purchased for in vitro assay and with compound 1 discovered to allosterically inhibit SERT (IC50 = 0.126 μM) when nomifensine was introduced as an orthosteric ligand. These results provide structural and energetic information for the molecular mechanism of serotonin reuptake and will provide opportunities for the development of novel therapeutics based on the identified new allosteric sites on SERT.  

 

Prof. Y-h. Taguchi
Chuo University, Japan

 

Y-h. Taguchi is a distinguished professor at the Department of Physics, Chuo University, Japan, where he specializes in applying cutting-edge methodologies like single-cell-based measurements to drug repositioning and genomic science. He earned his Bachelor of Science (B.S.) and Ph.D. degrees in physics from the Tokyo Institute of Technology in Tokyo, Japan. His scholarly contributions have been recognized and published in leading scientific journals, including Physical Review Letters, Bioinformatics, and Scientific Reports. Professor Taguchi is known for his development of a computational technique termed 'tensor decomposition-based unsupervised feature extraction'. This innovative approach has been successfully utilized for in silico phenotype-based drug discovery, particularly in repurposing known drugs for combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

 

Speech Title: "TDbasedUFE and TDbasedUFEadv: Bioconductor Packages to Perform Tensor Decomposition based Unsupervised Feature Extraction"

 

Abstract: Motivation: Tensor decomposition (TD)-based unsupervised feature extraction (FE) has proven effective for a wide range of bioinformatics applications ranging from biomarker identification to the identification of disease-causing genes and drug repositioning. However, TD-based unsupervised FE failed to gain widespread acceptance due to the lack of user-friendly tools for non-experts. Results: We developed two bioconductor packages—TDbasedUFE and TDbasedUFEadv—that enable researchers unfamiliar with TD to utilize TD-based unsupervised FE. The packages facilitate the identification of differentially expressed genes and multiomics analysis. TDbasedUFE was found to outperform two state-of-the-art methods, such as DESeq2 and DIABLO. Availability and implementation: TDbasedUFE and TDbasedUFEadv are freely available as R/Bioconductor packages, which can be accessed at https://bioconductor.org/packages/TDbasedUFE and https://bioconductor.org/packages/TDbasedUFEadv, respectively.

 

 

Previous Invited Speakers

Prof. Feng Guan
Northwest University, China

Dr. Feng Guan is a professor in the College of Life Sciences at Northwest University. He received his Ph.D. in Microbiology from China Agriculture University. After completing his Ph.D. he went to University of Washington, Seattle, as a postdoc supervised by Dr. Sen-itiroh Hakomori. In 2011, he moved back to China, and got a full professor position in Jiangnan University, China. And in 2017, he translocated to Northwest University, Xi'an. Dr. Guan's current research involves integrating omics techniques to identify dysregulated glycan chains during tumor development and progression, and investigating the role of these glycan chains in cell adhesion, growth, and apoptosis.

 

Prof. Yi Wang
Southwest University, China

Prof. Yi Wang currently is a professor and doctoral supervisor at the Biological Science Research Center, Southwest University. He received his bachelor's and doctoral degrees from Zhejiang University and Chongqing University, respectively, and conducted postdoctoral research at the University of California, Davis and Berkeley. His research interests include genomics, single-cell omics, and artificial intelligence. Professor Wang has led multiple national and provincial-level research projects and has published over 40 high-quality papers with a total citation count exceeding 3,300. His work has been recognized as highly cited papers in the ESI. He also serves as an editorial board member and reviewer for several international journals, and is an Associate Editor of iMeta journal.

 

Ms. Lei Zhang
Research Scientist in Bioinformatics and Omics Data at China National GeneBank DataBase, China

Lei Zhang received her B.S degree in computer science and M.S. degree in Biochemistry and Molecular Biology from HUST (Huazhong University of Science and Technology). She severed as project manager for Signal Transduction and Transcriptome Engineering in China-UK HUST-RRes Genetic Engineering and Genomics Joint Laboratory, executive director of Tivoli Education Technical Support in IBM GPSG RDC(Wuhan)(as internee). She joined CNGBdb as a research scientist in data curation and mining. Broad project experience in whole genome sequencing analysis of Model plants and animals, Genome-Wide Association Studies about Individuals and Groups, metagenome analysis of microbiome, rare disease associated SNPs detection and Pharmacogenomics research. She has published several academic papers and conference reports in scientific research, owns a number of invention patents. Current interests include biological big data mining, integration, management and sharing, aiming to provide all-in-one data service integrated data processing, data archiving, data online analysis and data application in Life Sciences.

 

Prof. Leyi Wei
Shandong University

Prof. Leyi Wei is currently a full Professor at School of Software, Shandong University, China. His research interests include bioinformatics and artificial intelligence. He has published 100+ peer-reviewed papers, receiving 4000+ citations in Google Scholar with h-index=40. His work has been recognized through the reception of awards, including Highly Cited Researcher" in Cross-Field (Released by Clarivate Analytics, 2021), ACM SIGBIO Rising Star Award (2021), and many others. He is now serving as Associate Editor and the Editorial Board member for a number of well-known journals, such as Frontiers in Genetics, Methods, BMC Genomics, and Current Bioinformatics, etc.

 

Assoc. Prof. Shirley Weng In Siu
University of Saint Joseph

Shirley Weng In Siu is an Associate Professor at the University of Saint Joseph. She received her PhD in Natural Sciences from Saarland University (Germany) in 2010. Between 2012 and 2021, she was Assistant Professor in the Department of Computer and Information Sciences at the University of Macau and the head of the Computational Biology and Bioinformatics Laboratory. Her research focuses on computational drug discovery, biomolecular simulation, cheminformatics and machine learning. She pioneers the application of swarm intelligence and machine learning to solve problems in protein ligand docking, drug target identification, and prediction of biological activity and toxicity. She is interested in finding new chemical and biological agents with pharmaceutical potential. Shirley also has great interest in the biophysics of proteins, membranes and polymers. Using molecular dynamics simulations, she has studied the conformational dynamics and mode of action of peptides that are antimicrobial, anticancer, and neurotoxic. She has been involved in the development of membrane lipid force fields and more recently in the modelling of self-assembling monolayers on biochips. Shirley is the author/co-author of more than 60 peer-reviewed journal and conference papers.

 

 

 

 

 

 

 

 

 

 

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