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Tucker BurginView profile

Assistant Professor

• Enzyme Engineering
• Molecular Simulation
• Computational Chemistry
• +3 more

Tucker Burgin is an Assistant Professor of Engineering at the Thayer School of Engineering, Dartmouth College. Her research focuses on computational enzyme engineering, integrating molecular simulations and machine learning to advance biotechnology solutions for health, energy, environmental sustainability, and green manufacturing. She earned a BS and MS in Biomedical Engineering from the University of Rochester (2015–2016) and a PhD in Chemical Engineering from the University of Michigan (2021), followed by a postdoctoral fellowship at the University of Washington. Her research interests include enzyme engineering, molecular simulation, computational chemistry, and machine learning applications. Current projects involve circular plastic bioeconomy systems, enzyme engineering for carbon capture, and improving oligosaccharide synthesis methods. The Burgin Lab develops novel methods to extract high-throughput data from atomistic simulations and applies AI-driven approaches to drive biotechnological innovations. She teaches ENGS 25: Introduction to Thermodynamics and ENGG 419: Deep Learning . The lab actively recruits PhD students and postdoctoral researchers, emphasizing interdisciplinary collaboration to address global challenges. Contact her at emme.burgin@dartmouth.edu for inquiries.

Luis Pedro CoelhoView profile

Associate Professor

• Computational Biology
• Microbiome Research
• Antimicrobial Peptides
• +3 more

Associate Professor Luis Pedro Coelho is a faculty member at Queensland University of Technology (QUT), holding positions in the School of Biomedical Sciences and serving as an ARC Future Fellow. His academic rank is Associate Professor. He earned a PhD in Computational Biology from Carnegie Mellon University, an MSc in Computer Science from Instituto Superior Técnico, and a BSc in Computer Science from the same institution. Research Interests: Coelho’s work focuses on computational biology, microbiome research, and the application of machine learning to study antimicrobial peptides and microbial diversity. His lab explores topics such as antibiotic resistance gene annotation, functional genomics, and bioinformatics tool development. His research has implications for understanding global microbiome dynamics and developing novel antimicrobial therapies. Teaching & Supervision : Coelho teaches scientific communication and software carpentry. He currently supervises research students in topics like microbial small protein discovery and computational methods for microbiome analysis. He has taught programming for scientists at Carnegie Mellon and is a certified Software Carpentry instructor. Awards : Highlights include the Highly Cited Researcher (2022-2023), Zhicheng Teaching Award (2021), Siebel Scholar (2012), and Fulbright Fellowship (2006). His work has been recognized for both academic excellence and innovation in teaching and research. Labs & Affiliations : He leads the Microbiome Research Group at QUT and is affiliated with the Centre for Microbiome Research. His collaborations span institutions like Fudan University (China), the European Molecular Biology Laboratory (EMBL), and the Instituto de Medicina Molecular (Portugal). Grants & Funding : His research is supported by ARC Future Fellowships and grants focused on sustainable bioinformatics software and microbial diversity studies.

Martin KircherView profile

Researcher

• Genomics
• Computational Biology
• Bioinformatics
• +7 more

Martin Kircher is a computational molecular biologist currently leading a research group at the Berlin Institute of Health (BIH), Germany. He previously held research positions at the University of Washington and the Max Planck Institute for Evolutionary Anthropology. His work spans genomics, bioinformatics, and functional genomics, with a focus on understanding the impact of genetic variation. Research Interests: His primary research areas include genomics, computational biology, and evolutionary genetics. He has made significant contributions to ancient DNA analysis, functional genomics, and the development of tools for interpreting non-coding variants. His work integrates high-throughput sequencing, machine learning, and molecular assays to decode regulatory elements and variant pathogenicity. Publication Trends: His recent publications reflect a strong focus on functional genomics, variant interpretation (e.g., CADD), and high-throughput methods like MPRA. He frequently publishes in top-tier journals and contributes to large consortia, indicating collaborative and impactful research in genomic medicine and regulatory biology. Scientific Contributions: He has developed widely used tools such as CADD and IBIS and contributed to landmark studies on Neandertal and Denisovan genomes. His protocols for sequencing library preparation are foundational in the field. Advising and Grants: While no formal list of advisees is provided, he leads a research group and has collaborated extensively. He has been involved in major projects such as the University of Washington's Center for Mendelian Genomics, suggesting substantial grant funding and collaborative leadership. Labs and Teams: He established and leads a computational research group at the Berlin Institute of Health. Previously, he was a key member of Jay Shendure’s lab at the University of Washington and part of Svante Pääbo’s team at the Max Planck Institute, contributing to large-scale genomics initiatives.

Feliu MaserasView profile

Professor

• Computational Chemistry
• Homogeneous Catalysis
• Density Functional Theory (DFT)
• +12 more

Feliu Maseras is a Professor and Group Leader at the Institute for Chemical Research of Catalonia (ICIQ-CERCA) , where he leads a research group focused on computational chemistry and its application to catalytic processes. His work bridges theoretical modeling and experimental collaboration, particularly in homogeneous catalysis, mechanochemistry, and photocatalysis. Education: PhD in Chemistry, Autonomous University of Barcelona (1991) Postdoctoral Training: Okazaki, Japan (Prof. Morokuma); Montpellier, France (Prof. Eisenstein) Academic Position: Associate Professor, UAB (1998–2004); Group Leader, ICIQ (2004–present) His research interests center on computational homogeneous catalysis , with a strong emphasis on DFT and QM/MM methods . He investigates cross-coupling , C–H activation , photocatalysis , mechanochemistry , and organic electrosynthesis . A key methodological focus is microkinetic modeling to connect computed energies with experimental kinetics, and the discovery of hidden descriptors using statistical analysis of large DFT datasets. The recent publications highlight a strong trend in mechanochemistry , photocatalysis , and transition metal catalysis , with increasing integration of artificial intelligence and data-driven methods . Many studies involve close collaboration with experimental groups, validating computational predictions. Scientific Awards: Rafael Usón Medal (2024) Miguel Catalán-Paul Sabatier Award (2024) Bruker Prize in Physical Chemistry (2011) Chemical Society Lecture Award, RSC (2008) Prof. Maseras has supervised 24 doctoral theses and hosted numerous postdoctoral researchers. His group is currently supported by grants such as the MECHAPHOTOCOMP project (Ministry of Science) and the Beatriu de Pinós fellowship. He serves as Associate Editor of ACS Catalysis and on the Editorial Advisory Board of Chemical Society Reviews . The research group includes PhD students, postdocs, technicians, and administrative staff, fostering a collaborative and interdisciplinary environment. Laboratory and Teams: The Maseras Group at ICIQ operates within a state-of-the-art research infrastructure, utilizing high-performance computing and collaborating with experimental teams across Europe. The group actively develops computational tools and models to advance understanding in sustainable synthesis and catalysis.

Kåre Lehmann NielsenView profile

Professor

• Gene sequencing and analysis
• Potato developmental biology
• Genomics
• +5 more

Kåre Lehmann Nielsen is a Professor in the Department of Chemistry and Bioscience at Aalborg University , associated with the Functional Ecology and Genomics group and the Center for Microbial Communities . His research focuses on plant genomics, microbial interactions, and agricultural biotechnology, with a strong emphasis on potato genetics, genome analysis, and functional genomics. Research Interests : Gene sequencing and analysis in plants and microbes Genetic variation and its impact on phenotypic traits Molecular breeding for crop improvement Microbial community dynamics and symbiosis Carbohydrate-active enzymes and secondary metabolites Recent Research Trends : Recent work highlights genomic prediction in tetraploid potatoes, the role of genetic variation in aging processes in Drosophila melanogaster , and the functional analysis of fungal enzymes. His studies often integrate transcriptomics, metagenomics, and bioinformatics to address challenges in plant breeding and microbial ecology. Grants and Projects : PI of projects like Sekventering og vedligeholdelse af MASPot kartoffelpopulationen (2023–2025) Collaborations on microbial community studies and disease-resistant potato development Labs and Teams : Lead researcher in the Functional Ecology and Genomics group, focusing on microbial communities and plant-microbe interactions. Active in the Center for Microbial Communities at Aalborg University.

Hao ZhouView profile

Associate Professor

• Generative AI
• Molecular Modeling
• Protein Engineering
• +3 more

Hao Zhou is a Research Associate Professor at the Institute for AI Industry Research (AIR), Tsinghua University. His research focuses on developing Generative AI models for discrete symbols such as text, small molecules, and proteins. Previously, he worked as a Research Scientist/Manager at Bytedance AI Lab. Currently, he leads the Generative Symbolic Intelligence group at Tsinghua AIR and co-leads the SIA Lab, a joint laboratory with ByteDance for LLM research. His research interests span molecular representation learning, generative models for scientific discovery, and applications in drug design and protein engineering. Recent work includes advancements in 3D molecular modeling, protein language models, and non-autoregressive sequence generation. These efforts aim to bridge AI techniques with scientific domains like chemistry and biology. Recent publications highlight contributions to areas such as auto-encoder-based molecular representation (Mol-AE), multi-scale protein modeling (ESM All-Atom), and structure-based drug design (MolCRAFT). These papers reflect a focus on improving generative models' accuracy and applicability to real-world scientific challenges. Best Short Paper of INLG (2022) Ranked #1 on WMT EN-DE translation (2021) CCF NLPCC Distinguished Young Scientist (2021) Best Paper Award of ACL (2021) CAAI Doctoral Dissertation Award (2019) He advises several students, including Qiying Yu, Yuxuan Song, and Danqing Wang, and oversees grants related to AI in drug discovery and molecular modeling. His labs are actively hiring postdocs and research assistants, and he encourages student interns to join collaborative projects. Key initiatives include the SIA Lab’s work on LLM research and the Generative Symbolic Intelligence group’s exploration of AI-driven scientific breakthroughs. These efforts emphasize interdisciplinary approaches to tackle complex challenges in AI and life sciences.

Dima KozakovView profile

Research Professor

• Macromolecular recognition
• Protein-protein interaction networks
• Drugability of genome
• +3 more

Dima Kozakov is a Research Professor in the Department of Biomedical Engineering at Boston University. He holds a PhD in Biomedical Engineering from Boston University and an M.Sci. in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology. His research focuses on macromolecular recognition, signaling protein-protein interaction networks, genome drugability, and protein design. He is affiliated with the Research Faculty at BU and has contributed to advancements in computational methods for structural biology and drug discovery. His work integrates advanced Fourier and optimization techniques to explore macromolecular interaction landscapes, including structure determination of macromolecular complexes from low-resolution data and validation of protein interaction networks. He develops methods for fragment-based drug design, identifying cryptic binding sites amenable to small molecule inhibition in cancer pathways, and designs peptidic chaperones for crystallization. His contributions include the ClusPro docking server and FTMap pharmacophore identification tools. Recent publications highlight his exploration of ligand interaction landscapes in E. coli, targeted protein degradation mechanisms, and AlphaFold-based structural predictions. He investigates multiomic profiles of immune cells and metabolic reprogramming in disease contexts. His research emphasizes druggable site identification, computational modeling of protein interactions, and advancing machine learning applications in structural biology. Key innovations include MHC-Fine for precise MHC-peptide complex prediction and E-FTMap for fragment-based drug discovery. His work bridges computational methods with experimental validation, addressing challenges in protein structure prediction and drug target identification. Kozakov collaborates on projects like the CASP15 CAPRI experiment, assessing computational methods for protein complex modeling.

Justin SiegelView profile

Professor

• Protein Engineering
• Enzymology
• Computational Biology
• +3 more

Justin Siegel is a Professor at the University of California, Davis, affiliated with the Genome and Biomedical Sciences Facility. His research focuses on computational and experimental protein engineering, particularly designing novel enzymes for therapeutic and industrial applications. He pioneered the de novo design of enzymes performing non-natural reactions like the Diels-Alder reaction and collaborated with Foldit gamers to enhance enzyme activity. Recent work includes gluten-degrading enzymes, CO2 fixation pathways, and biofuel production. He holds a PhD from the University of Washington (2011) and a BS from UC Davis (2005). Research Highlights: Developed Rosetta computational tools for macromolecular design Engineered anthrax therapeutics and gluten-digesting enzymes Pioneered noncanonical cofactor-based biocatalytic systems Investigates sustainable protein production and cultured meat economics Awards: Sloan Research Fellowship (2014), UC Davis Faculty Appointment (2012). His work spans 80+ publications and multiple patent families, with ongoing projects in metabolic engineering and drug discovery.

Lee-Ping WangView profile

Associate Professor

• Theoretical Chemistry
• Computational Chemistry
• Reaction Mechanisms
• +5 more

Lee-Ping Wang is an Associate Professor in the Department of Chemistry at the University of California, Davis, with expertise in theoretical and computational chemistry. Their research focuses on accelerating reaction pathway discovery, understanding catalytic mechanisms, and developing advanced molecular simulation tools. Ph.D. in Physical Chemistry, MIT (2011) B.A. in Physics, University of California, Berkeley (2006) Research interests span three major areas: (1) CO 2 reduction mechanisms in catalysts, (2) proton transfer in membrane proteins, and (3) surface reconstruction of perovskite electrocatalysts and photovoltaics. Their work integrates quantum chemistry, molecular dynamics, and machine learning to design predictive force fields and study energy conversion processes. Recent research trends include ab initio nanoreactors for reaction discovery, polarizable force fields for accurate simulations, and quantum mechanical benchmarking to improve computational models. These efforts address challenges in renewable energy, biochemistry, and materials science. Scientific Award : ACS-PRF Doctoral New Investigator (2017) They lead the Wang Group , developing computational tools like Open Force Field Evaluator and TeraChem for high-performance molecular simulations. Their publications emphasize data-driven methodologies, reaction mechanism elucidation, and interdisciplinary applications bridging chemistry and physics.

• Bioinformatics
• Systems Biology
• Computational Biology
• +3 more

Prof. Dr. Ralf Zimmer is a Full Professor (C4) of Practical Informatics with a focus on Bioinformatics at Ludwig Maximilian University of Munich (LMU). He leads the Bioinformatics Teaching and Research Unit within the Institute of Computer Science. His academic journey includes a PhD from the Christian-Albrechts-Universität Kiel (summa cum laude) and postdoctoral research at the National Cancer Institute (USA) and Stanford University. His research integrates computational methods with biological systems, emphasizing systems biology, RNA processing, and viral-host interactions. Education: Diplom in Computer Science (1986), PhD (1990). Key roles include Director of the DFG Bioinformatics Center Munich and spokesperson for the RECESS Research Training Group. His work spans protein interaction networks, transcriptome analysis, and computational tools like ContextMap and FERN . Research interests include bioinformatics , systems biology , RNA dynamics , and viral infection mechanisms . Over 30 students have completed theses under his supervision, contributing to fields like alternative splicing and computational modeling. Publications highlight innovations in RNA-seq analysis, proteotoxic stress responses, and viral regulatory networks. Awards include the 1990 Dissertation Prize from CAU Kiel. He advises on grants and leads interdisciplinary teams in Munich’s Bioinformatics programs and international collaborations.

Karen T. HastingsView profile

Professor

• Skin Cancer
• Immunology
• Dermatology
• +6 more

Karen T. Hastings is a Professor and Inaugural Chair of the Department of Dermatology at the University of Arizona College of Medicine - Phoenix . Her research program focuses on antigen presentation , T cell responses , and neoantigen dynamics in skin cancer (melanoma and cutaneous squamous cell carcinoma). She leads a multidisciplinary team combining immunological , bioinformatic , and molecular biological approaches. Education: MD, PhD (Immunology) - Harvard University (2000) Residency - Yale New Haven Hospital (Dermatology, 2004) Postdoctoral Fellowship - Yale University (Immunobiology, 2006) Research Interests encompass skin cancer immunobiology , antigen processing , immune checkpoint regulation , and bioinformatics modeling of neoantigen landscapes. Her lab has defined GILT as a critical enzyme in MHC class II antigen presentation and investigates its role in tumor-immune interactions using: Genetically engineered mouse models Human tumor specimen analysis Transcriptomic profiling Multi-omics integration Immune cell deconvolution Her scientific contributions include: Elucidating GILT's role in central T cell tolerance Developing neoantigen prediction frameworks Characterizing immune infiltration patterns in viral-mediated HCC Identifying sex-based molecular differences in HCC Trainees under her mentorship have received NIH F30 grants , Medical Student Awards , and travel awards to international conferences. The Hastings Lab collaborates with researchers at Arizona State University and participates in translational cancer research initiatives.

Colin FishwickView profile

Professor

• Medicinal Chemistry
• Synthetic Methods
• Chemical Biology
• +12 more

Professor Colin Fishwick is a faculty member at the University of Leeds, where he serves as Professor of Medicinal Chemistry and Head of the School of Chemistry. He is also an Honorary Visiting Professor at the University of Bradford’s Institute of Cancer Therapeutics and at the University of Sheffield. His work bridges computational drug design and synthetic chemistry to address major global health challenges. Research Interests: His research focuses on medicinal chemistry, synthetic methods, chemical biology, drug design, and molecular modelling. He pioneers computational tools like SPROUT for fragment-based design and applies them to discover novel ligands for challenging targets. His work spans antimicrobial resistance (particularly against ESKAPE pathogens), neglected tropical diseases (malaria, toxoplasmosis), cardiovascular repair, and diabetes therapeutics. He integrates cryo-EM, computational biology, and synthetic chemistry to develop selective small molecules and chemical probes. Publication Trends: His recent publications reflect a strong emphasis on combating antibiotic resistance through dual-targeting strategies and novel enzyme inhibitors. He also advances anti-parasitic drug development, particularly DHODH and mPPase inhibitors, and explores insulin signalling restoration for diabetes. His work increasingly incorporates solid-form property prediction for improved drug bioavailability. Scientific Awards: No specific awards mentioned in the text. Advising and Grants: He leads a research group in Chemical Biology and Medicinal Chemistry and welcomes PhD students. He collaborates with Medicines for Malaria Venture (MMV) and the Leeds Institute of Cardiovascular and Molecular Medicine (LICAMM), indicating involvement in externally funded projects. While specific grants are not listed, his international collaborations and project descriptions imply sustained funding. Labs and Teams: He leads the research group focused on computational and synthetic approaches to drug discovery within the School of Chemistry. He is affiliated with the Astbury Centre and collaborates across disciplines, including parasitology, cardiovascular medicine, and structural biology.

Katie SimmonsView profile

Lecturer

• Structural Pharmacology
• Medicinal Chemistry
• Chemical Biology
• +7 more

Dr. Katie Simmons is a Lecturer in Structural Pharmacology at the School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds. Her research integrates computational and experimental approaches to identify bioactive molecules for early-stage drug discovery, targeting diseases such as Type 2 Diabetes, epilepsy, and cardiometabolic disorders. Research Interests: Medicinal Chemistry and Ligand Design Molecular Modelling and In Silico Screening Chemical Biology and Fragment-Based Discovery Molecular Biology and Assay Development Targeting Integrins, Ion Channels, and Receptor Hybrids Her work emphasizes the development of therapeutic leads through collaborations within the University of Leeds and with industrial partners. Key projects include RGD-mimetics for insulin sensitization, fluorine-containing inhibitors for cardiovascular protection, potassium channel blockers for epilepsy, and modulators of IR-IGF1R hybrids. Research Trends in Publications: Her recent scholarly output centers on early-stage drug discovery, combining computational prediction with synthetic chemistry and biophysical validation. Articles reflect a strong focus on structural pharmacology, targeting protein-protein interactions, receptor modulation, and fragment-based screening, particularly using NMR and in silico methods. Scientific Contributions: Lead discovery for cardiometabolic diseases Development of novel therapeutics for rare epilepsies Innovation in fluorine-based NMR screening Tool compound generation for hybrid receptor studies Advising and Research Support: Dr. Simmons leads an active research group focused on structural pharmacology and drug discovery. She mentors students and researchers in interdisciplinary projects combining computational modelling, synthetic chemistry, and molecular biology. Her work is supported by academic collaborations and aims to translate findings into industrial partnerships for further development. Laboratory and Research Environment: Based in Garstang 6.44F at the University of Leeds, her lab utilizes a range of techniques including molecular modelling, small molecule synthesis, assay development, and biophysical methods such as NMR. The group collaborates widely across institutions to advance therapeutic leads.

Wesley TanseyView profile

Assistant Professor

• Statistical Machine Learning
• Computational Oncology
• Bayesian Statistics
• +3 more

Wesley Tansey serves as Assistant Professor in the Computational Oncology group within the Department of Epidemiology and Biostatistics at Memorial Sloan Kettering Cancer Center (MSKCC). His research bridges statistical machine learning with cancer biology, focusing on developing novel computational frameworks for oncology applications. Dr. Tansey's research program centers on Bayesian statistical methods for biological data analysis, with particular emphasis on spatial transcriptomics (evidenced by his BayesTME framework), drug response modeling , and multi-omics integration . His lab develops scalable algorithms for high-dimensional biological data, including UnitedMet for metabolite imputation and MultiTME for spatial profiling analysis. Current projects address combinatorial drug screening optimization, tumor microenvironment characterization, and predictive oncology platforms for rare cancers. His recent publications (2023-2025) demonstrate strong focus areas: Bayesian active learning for drug screening (6+ publications) Spatial biology methods (BayesTME, MultiTME) Metabolomics-transcriptomics integration (UnitedMet) Causal inference in biological systems Scientific recognition includes serving as Area Chair for AISTATS 2022 and frequent invited talks at major conferences including SIAM's Mathematics of Data Science meeting. Dr. Tansey actively mentors lab members including Sophie Jaro (Spotlight presenter at ICML Workshop), Haoran Zhang (contributed talk presenter), Christopher Tosh (Associate Research Scientist), and Jeff Quinn (Bioinformatics Software Engineer). His lab receives research funding supporting development of computational oncology platforms with clinical translation potential. The VIVO Lab maintains active GitHub repositories for core methodologies including BayesTME, reflecting strong software engineering practices in computational biology.

Natalie BorgView profile

Associate Professor

• Innate immunity
• Microbial pathogenesis
• Antiviral drug discovery
• +2 more

Associate Professor Natalie Borg is a faculty member in the Department of Health and Biomedical Sciences at RMIT University , Australia. She leads the Immunity and Immune Evasion Laboratory and holds the title of RMIT Senior Research Translation Fellow . Her research focuses on innate immunity and how pathogens manipulate immune responses. This work drives antimicrobial drug discovery programs and investigates nanostructured surface applications for viral inactivation. Scientific Awards NHMRC Peter Doherty Fellowship NHMRC Career Development Award L'Oreal for Women in Science Fellowship ARC Future Fellowship Her research programs have secured $3.65 million in competitive national funding from ARC, NHMRC, and the US Leukemia and Lymphoma Society, alongside $1.16 million in translational grants supporting multidisciplinary collaborations and a technology licensing agreement.