HUBioDataLab · MelihDarcanxyz · Oct 25, 2025 · Oct 25, 2025 · Oct 25, 2025 · Oct 25, 2025
diff --git a/_config.yaml b/_config.yaml
@@ -8,12 +8,10 @@ proofer: false
 
 # site social media and other links
 links:
-  email: contact@HUBioDataLab.com
-  orcid: 0000-0001-8713-9213
-  google-scholar: ETJoidYAAAAJ
-  github: HUBioDataLab
-  twitter: HUBioDataLab
-  youtube: HUBioDataLab
+  email: tuncadogan@gmail.com
+  orcid: https://orcid.org/0000-0002-1298-9763
+  google-scholar: tHnMNPEAAAAJ
+  github: hubiodatalab
 
 ### jekyll settings
 

diff --git a/_includes/footer.html b/_includes/footer.html
@@ -39,4 +39,4 @@
     aria-label="toggle dark mode"
     oninput="onDarkToggleChange(event)"
   >
-</footer>
+</footer> 
diff --git a/_includes/post-info.html b/_includes/post-info.html
@@ -42,4 +42,4 @@
 
 {% if include.tags %}
   {% include tags.html tags=include.tags link="blog" %}
-{% endif %}
+{% endif %} 
diff --git a/_layouts/member.html b/_layouts/member.html
@@ -39,13 +39,13 @@
 </p>
 
 {% capture search -%}
-  blog/?search={{ page.name }}
+  news/?search={{ page.name }}
 {%- endcapture %}
 
 <!--
 <p class="center">
   <a href="{{ search | relative_url | uri_escape }}">
-    See {{ page.name | default: page.title }}'s posts on the Blog page
+    See {{ page.name | default: page.title }}'s posts on the News page
   </a>
 </p>
--->
+-->
diff --git a/_posts/2019-01-07-example-post-1.md b/_posts/2019-01-07-example-post-1.md
diff --git a/_posts/2021-09-30-example-post-2.md b/_posts/2021-09-30-example-post-2.md
diff --git a/_posts/2023-02-23-example-post-3.md b/_posts/2023-02-23-example-post-3.md
diff --git a/_posts/2023-07-23-ismb-eccb2023-participation.md b/_posts/2023-07-23-ismb-eccb2023-participation.md
@@ -0,0 +1,18 @@
+---
+title: ISMB/ECCB 2023 Participation
+author: research-group
+tags:
+  - conference
+  - bioinformatics
+  - computational biology
+  - ISMB
+  - ECCB
+---
+
+Our team from Hacettepe University Biological Data Science Laboratory participated in the International Conference on Intelligent Systems for Molecular Biology (ISMB) and the European Conference on Computational Biology (ECCB 2023).
+
+ISMB/ECCB 2023, held in Lyon, France, is the largest and most high-profile annual meeting of scientists working in computational biology. This premier event provided an intense multidisciplinary forum for disseminating the latest developments in computational tools for data-driven biological research.
+
+The conference offered an excellent opportunity for our laboratory to present our research in machine learning, deep learning, and bioinformatics, while engaging with the global computational biology community.
+
+![ISMB/ECCB 2023 Participation]({{ '/images/news/ismb2023.jpg' | relative_url }})
diff --git a/_posts/2024-09-16-eccb2024-participation.md b/_posts/2024-09-16-eccb2024-participation.md
@@ -0,0 +1,18 @@
+---
+title: ECCB 2024 Participation
+author: research-group
+tags:
+  - conference
+  - bioinformatics
+  - computational biology
+  - ECCB
+  - Finland
+---
+
+Our team from Hacettepe University Biological Data Science Laboratory participated in the 23rd European Conference on Computational Biology (ECCB 2024).
+
+ECCB 2024, held in Turku, Finland, brought together scientists from a wide range of disciplines including computational biology, systems biology, bioinformatics, artificial intelligence, biology, and medicine. The conference focused on data and algorithms for health and science, providing an excellent platform for multidisciplinary collaboration.
+
+The event featured top keynotes, talks, lively poster sessions, and exhibitions, offering our laboratory valuable opportunities to present our research in machine learning, deep learning, and bioinformatics while engaging with the European computational biology community.
+
+![ECCB 2024 Participation]({{ '/images/news/eccb2024.jpg' | relative_url }})
diff --git a/_posts/2024-12-18-hibit2024-participation.md b/_posts/2024-12-18-hibit2024-participation.md
@@ -0,0 +1,15 @@
+---
+title: HIBIT 2024 Participation
+author: research-group
+tags:
+  - conference
+  - bioinformatics
+  - health informatics
+  - HIBIT
+---
+
+Our team from Hacettepe University Biological Data Science Laboratory participated in the 17th International Symposium on Health Informatics and Bioinformatics (HIBIT 2024).
+
+HIBIT, held since 2005, is an important conference that aims to create synergy between medical, biological, and information technology sectors. It provided a valuable platform for presentations and discussions on machine learning, deep learning, and bioinformatics topics that align with our laboratory's research areas.
+
+![HIBIT 2024 Participation]({{ '/images/news/hibit2024.jpg' | relative_url }})
diff --git a/_posts/2025-07-20-ismb-eccb2025-participation.md b/_posts/2025-07-20-ismb-eccb2025-participation.md
@@ -0,0 +1,18 @@
+---
+title: ISMB/ECCB 2025 Participation
+author: research-group
+tags:
+  - conference
+  - bioinformatics
+  - computational biology
+  - ISMB
+  - ECCB
+---
+
+Our team from Hacettepe University Biological Data Science Laboratory participated in the 33rd International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 24th European Conference on Computational Biology (ECCB 2025).
+
+ISMB/ECCB 2025, held in Liverpool, UK, is the world's largest bioinformatics and computational biology conference. This premier event brings together researchers from computer science, bioinformatics, computational biology, molecular biology, mathematics, and statistics to share the latest advancements in computational methods for addressing biological problems.
+
+The conference provided an excellent platform for our laboratory to present our research in machine learning, deep learning, and bioinformatics, while connecting with the global computational biology community.
+
+![ISMB/ECCB 2025 Participation]({{ '/images/news/ismb2025.jpg' | relative_url }})
diff --git a/_posts/2025-09-20-chempile-dataset-neurips.md b/_posts/2025-09-20-chempile-dataset-neurips.md
@@ -0,0 +1,15 @@
+---
+title: "ChemPile Dataset Paper Accepted at NeurIPS 2025"
+author: research-news
+tags:
+  - dataset
+  - chemistry
+  - AI
+  - foundation models
+  - NeurIPS
+---
+
+Great news! The paper "ChemPile: A 250 GB Diverse and Curated Dataset for Chemical Foundation Models," co-authored by Bünyamin Şen and led by a research group in Germany, has been accepted to the NeurIPS 2025 conference.
+
+This is a significant development, as NeurIPS is one of the world's most prestigious CS/AI conferences. The paper introduces ChemPile, a massive (250 GB, 75B tokens) open dataset of curated chemical data, designed to train and evaluate the next generation of chemical foundation models. 
+
diff --git a/_posts/2025-09-26-turkish-universities-druggen-ai-project.md b/_posts/2025-09-26-turkish-universities-druggen-ai-project.md
@@ -0,0 +1,31 @@
+---
+title: "Turkish Universities Accelerate Drug Discovery with 'DrugGEN' AI Project"
+author: research-news
+tags:
+  - AI
+  - drug discovery
+  - generative AI
+  - Hacettepe University
+  - METU
+  - Gazi University
+  - Nature
+---
+
+A collaborative project by scientists from Hacettepe, Gazi, and Middle East Technical University (METU) is using artificial intelligence to accelerate new drug development processes. The project, named 'DrugGEN', not only aims to shorten the years-long drug development timeline but also to reduce its high costs. The research was published in *Nature*, one of the world's most prestigious scientific journals.
+
+The study, which began in 2021 and involved 11 faculty members, initially focused on developing treatments for liver cancer. Prof. Dr. Tunca Doğan of Hacettepe University's Department of Computer Engineering explained the work.
+
+"Our AI model can rapidly develop a molecule, provided that experts first tell us which protein, naturally found in our body, needs to be interacted with to treat the disease," said Prof. Dr. Doğan. "We were inspired by large language models like ChatGPT. Just as language models produce a correct and meaningful answer to a question, our model generates molecules for a user-defined protein target."
+
+### Successful Results in Cancer Study
+
+In their research, the team focused on liver cancer, based on expertise from METU's Cancer System Biology Laboratory. "They told us which protein we should develop a molecule for based on their research data," Doğan stated. "The AI then designed many molecules."
+
+After successful computational tests, the team moved to the experimental phase. "Out of the 5 molecules produced by the AI, two managed to bind to the protein," Doğan reported. These molecules were then tested on laboratory cell lines, where they showed the same effect. Doğan noted that human trials are the next necessary, complex step.
+
+### Goal: Reducing 15 Years to 2
+
+Prof. Dr. Doğan highlighted the inefficiencies of current methods, where drug companies test massive libraries of molecules over long periods. "The time it takes for a single drug from its starting point to being available in a pharmacy is, on average, 15 years. Its current cost is around $2 billion."
+
+"With AI," he continued, "we aim to reduce the pre-human trial period to 1-2 years and lower the cost, for example, to $1 million. We also want this to be a study that not only a few large pharmaceutical companies in the world can do, but for Turkey to be able to develop its own drugs."
+
diff --git a/_posts/2025-10-15-ai-driven-poxvirus-inhibitors.md b/_posts/2025-10-15-ai-driven-poxvirus-inhibitors.md
@@ -0,0 +1,13 @@
+---
+title: AI-Driven Study Finds Potential Poxvirus Inhibitors
+author: research-group
+tags:
+  - AI
+  - drug discovery
+  - virology
+  - medicine
+---
+
+Great news from our group! Our collaborative study with Erasmus University, focusing on drug development against the pox virus, has been accepted by the journal *Communications Biology*. Congratulations to Atabey and Elif for their contributions!
+
+The study, "AI-driven discovery of the antiretroviral drug bictegravir and etravirine as potent inhibitors against monkeypox and related poxviruses," used an AI pipeline to identify these two existing drugs as potent inhibitors of MPXV and other poxviruses. This discovery supports their potential repurposing for treating mpox, especially for high-risk patients.
diff --git a/_posts/2025-10-21-druggen-nature-machine-intelligence.md b/_posts/2025-10-21-druggen-nature-machine-intelligence.md
@@ -0,0 +1,37 @@
+---
+title: "DrugGEN Paper on Generative AI for Drug Design Published in Nature Machine Intelligence"
+author: research-news
+tags:
+  - AI
+  - generative AI
+  - drug discovery
+  - bioinformatics
+  - Hacettepe University
+  - Nature Machine Intelligence
+---
+
+A paper detailing "DrugGEN," a novel system for designing target-specific drug candidate molecules using generative AI, has been published in *Nature Machine Intelligence*, one of the world's leading scientific journals for artificial intelligence.
+
+The research was conducted by a team from the Hacettepe Biological Data Science Lab., led by Prof. Dr. Tunca Doğan from Hacettepe University's Department of Computer Engineering and Head of the Institute of Informatics, Health Informatics A.B.D.
+
+The DrugGEN system presents a novel architecture that, for the first time, integrates Generative Adversarial Networks (GANs) with graph transformer-based deep learning methods. This combined architecture leverages both the powerful feedback mechanism of GANs and the sophisticated modeling capabilities of graph transformers to automatically design *de novo* drug candidate molecules conditioned on a specific protein target.
+
+### Contribution to Cancer Research
+
+In the study, the team targeted the AKT1 protein, which is associated with many different types of cancer. The AI designed thousands of potential molecules, five of which were synthesized and tested in the laboratory. Two of these molecules successfully suppressed the target protein at a significant level. Computational analyses, including attention maps and molecular dynamics, confirmed that the model was able to capture the intended target-specific interactions.
+
+### Why It Matters
+
+The study demonstrates that generative AI not only provides speed and cost advantages in the drug discovery pipeline but can also identify completely novel molecule candidates that have not been discovered before. This capability promises significant contributions to the development of new treatments for diseases.
+
+In the interest of scientific transparency and public benefit, all code, pre-trained models, datasets, and result outputs have been made available via open access.
+
+The research was led by Hacettepe University with participation from METU and Gazi University, and was supported by the TÜBİTAK 2247 National Leading Researchers Program. The study's completion entirely within Turkey highlights the country's capability for high-quality research at the intersection of generative AI and health.
+
+**Links**
+
+* Article Link: [https://www.nature.com/articles/s42256-025-01082-y](https://www.nature.com/articles/s42256-025-01082-y)
+* Free Full-Text Access: [https://rdcu.be/eGoHv](https://rdcu.be/eGoHv)
+* Source Code, Data, Models: [https://github.com/HUBioDataLab/DrugGEN](https://github.com/HUBioDataLab/DrugGEN)
+* Demo: [https://huggingface.co/spaces/HUBioDataLab/DrugGEN](https://huggingface.co/spaces/HUBioDataLab/DrugGEN)
+
diff --git a/_posts/2025-10-23-tunca-dogan-purdue-seminar.md b/_posts/2025-10-23-tunca-dogan-purdue-seminar.md
@@ -0,0 +1,23 @@
+---
+title: "Prof. Dr. Tunca Doğan Presents Generative AI Research at Purdue University Seminar"
+author: research-news
+tags:
+  - AI
+  - generative AI
+  - drug discovery
+  - bioinformatics
+  - seminar
+  - Hacettepe University
+  - Purdue University
+---
+
+Prof. Dr. Tunca Doğan, a faculty member at Hacettepe University's Department of Computer Science and Artificial Intelligence Engineering, delivered a presentation at the prestigious Purdue University Online Bioinformatics Seminar Series on October 23.
+
+The seminar, titled "Harnessing Generative AI for Biomedical Discovery: Design, Integration, and Insight," highlighted the Hacettepe Biological Data Science Laboratory's efforts in using AI-driven methodologies to tackle modern biomedical challenges.
+
+During the talk, Prof. Dr. Doğan introduced DrugGEN, the lab's end-to-end graph-transformer-based generative adversarial network for designing target-specific drug candidate molecules. He noted that DrugGEN has successfully generated novel inhibitors for the AKT1 protein, which have been validated *in vitro*.
+
+The presentation also briefly covered FlowProt, a classifier-guided flow-matching model for designing protein backbones, and CROssBARv2, a platform that unifies biomedical data into a queryable knowledge graph, enhanced with LLM-powered natural language interfaces.
+
+Prof. Dr. Doğan, who leads the Hacettepe Biological Data Science Laboratory, was a postdoctoral researcher at EMBL-EBI and Cambridge University before his current role. His research focuses on developing machine and deep learning methods for biomedical data integration, functional prediction, and drug discovery. The seminar was hosted by Prof. Daisuke Kihara of Purdue University.
+
diff --git a/images/news/eccb2024.jpg b/images/news/eccb2024.jpg
diff --git a/images/news/hibit2024.jpg b/images/news/hibit2024.jpg
diff --git a/images/news/ismb2023.jpg b/images/news/ismb2023.jpg
diff --git a/images/news/ismb2025.jpg b/images/news/ismb2025.jpg
diff --git a/images/projects.jpg b/images/projects.jpg
diff --git a/images/research.jpg b/images/research.jpg
diff --git a/images/team.jpg b/images/team.jpg
diff --git a/index.md b/index.md
@@ -1,17 +1,22 @@
 ---
 ---
 
-# HUBioDataLab's Website
+# Hacettepe University Biological Data Science Laboratory
 
-An engaging 1-3 sentence description of your lab.
+Hacettepe Biological Data Science Lab's work focuses on developing machine/deep learning-based methods for:
+i. the integration and representation of heterogeneous biomedical data
+ii. the prediction of the functional properties of genes/proteins
+iii. discovering/designing new drug candidates
+
+We frequently utilise representation learning and generative artificial intelligence. We have multiple ongoing projects focused on small molecule and protein design.
 
 {% include section.html %}
 
 ## Highlights
 
 {% capture text %}
 
-Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
+We apply cutting-edge machine learning and deep learning to decode complex biological and chemical data. Our research focuses on novel AI methods for drug discovery, protein function prediction, and molecular representation learning.
 
 {%
   include button.html
@@ -26,15 +31,15 @@ Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor i
 
 {%
   include feature.html
-  image="images/photo.jpg"
+  image="images/research.jpg"
   link="research"
   title="Our Research"
   text=text
 %}
 
 {% capture text %}
 
-Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
+Our lab builds and applies novel computational tools to real-world challenges. Our projects range from generative models for novel drug design to advanced graph networks for analyzing protein-target interactions.
 
 {%
   include button.html
@@ -49,7 +54,7 @@ Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor i
 
 {%
   include feature.html
-  image="images/photo.jpg"
+  image="images/projects.jpg"
   link="projects"
   title="Our Projects"
   flip=true
@@ -59,7 +64,7 @@ Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor i
 
 {% capture text %}
 
-Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
+Our lab is powered by an interdisciplinary team of computational biologists, data scientists, and engineers. We are passionate about using AI for science and fostering a collaborative research environment.
 
 {%
   include button.html
@@ -74,7 +79,7 @@ Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor i
 
 {%
   include feature.html
-  image="images/photo.jpg"
+  image="images/team.jpg"
   link="team"
   title="Our Team"
   text=text

diff --git a/news/index.md b/news/index.md
@@ -0,0 +1,20 @@
+---
+title: News
+nav:
+  order: 4
+  tooltip: Latest news and updates
+---
+
+# News
+
+Stay updated with the latest news, announcements, and updates from Hacettepe University Biological Data Science Laboratory.
+
+{% include section.html %}
+
+{% include search-box.html %}
+
+{% include tags.html tags=site.tags %}
+
+{% include search-info.html %}
+
+{% include list.html data="posts" component="post-excerpt" %}
diff --git a/research/index.md b/research/index.md
@@ -7,14 +7,13 @@ nav:
 
 # {% include icon.html icon="fa-solid fa-microscope" %}Research
 
-Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
-Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
+Our lab focuses on the application of machine learning and deep learning to complex challenges in bioinformatics and cheminformatics. We specialize in developing novel computational methods for protein function prediction, drug-target interaction analysis, and de novo molecular design, aiming to accelerate the pace of scientific discovery.
 
 {% include section.html %}
 
 ## Highlighted
 
-{% include citation.html lookup="Open collaborative writing with Manubot" style="rich" %}
+{% include citation.html lookup="doi:10.1038/s42256-025-01082-y" style="rich" %}
 
 {% include section.html %}