Update publications: Fix the bibtex entries in the publication section

Author: HFooladi

_publications/2013-10-01-walking-biped.md

@@ -17,7 +17,7 @@ In this research, a deployed model of biped that can be built has been considere
 
 [researchgate](https://www.researchgate.net/publication/258746206_Optimum_Design_Manufacturing_and_Experiment_of_a_Passive_Walking_Biped_Effects_of_Structural_Parameters_on_Efficiency_Stability_and_Robustness_on_Uneven_Trains), [Applied Mechanics and Materials](https://www.scientific.net/AMM.307.107)
 
-```{bibtex}
+```bibtex
 @article{sadati2013optimum,
   title={Optimum Design, Manufacturing and Experiment of a Passive Walking Biped: Effects of Structural Parameters on Efficiency, Stability and Robustness on Uneven Trains},
   author={Sadati, SM Hadi and Borgheinejad, M and Fooladi, H and Naraghi, M and Ohadi, AR},

_publications/2019-03-01-waddington_-landscape.md

@@ -29,7 +29,7 @@ This model provides a basis for in-silico modeling of self-organization.
 
 [https://doi.org/10.1093/bioinformatics/btz201](https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz201/5418791?redirectedFrom=fulltext), [bioRxiv](https://www.biorxiv.org/content/10.1101/241604v1), [github](https://github.com/HFooladi/Self_Organization)
 
-```{bibtex}
+```bibtex
 @article{fooladi2019enhanced,
   title={Enhanced Waddington landscape model with cell--cell communication can explain molecular mechanisms of self-organization},
   author={Fooladi, Hosein and Moradi, Parsa and Sharifi-Zarchi, Ali and Hosein Khalaj, Babak},

_publications/2019-03-02-arrow-of-time.md

@@ -22,7 +22,7 @@ outperforms RNN-based architectures.
 
 [github](https://github.com/ShenakhtPajouh/transposition-data). [arXiv](https://arxiv.org/abs/1903.10548v1), Accepted at WiNLP 2019 workshop
 
-```{bibtex}
+```bibtex
 @article{hosseini2019recognizing,
   title={Recognizing Arrow Of Time In The Short Stories},
   author={Hosseini, Fahimeh and Fooladi, Hosein and Samsami, Mohammad Reza},

_publications/2023-12-01-botcp.md

@@ -30,7 +30,7 @@ demonstrate that even with a clustering that requires members to have a high sim
 those clusters that contain poses close to the experimentally determined native structure of the ternary complexes. 
 We also demonstrate the resultant improved yield of near-native poses3 in these clusters.
 
-```{bibtex}
+```bibtex
 @article{rao2023bayesian,
   title={Bayesian optimization for ternary complex prediction (BOTCP)},
   author={Rao, Arjun and Tunjic, Tin M and Brunsteiner, Michael and M{\"u}ller, Michael and Fooladi, Hosein and Gasbarri, Chiara and Weber, Noah},

_publications/2023-12-18-PLI.md

@@ -14,7 +14,7 @@ Nedra Mekni, **Hosein Fooladi**, Ugo Perricone , and Thierry Langer
 
 To address the challenge, we introduce a model called InterGraph, which models the protein-ligand interaction as topological multigraphs. By leveraging a topological representation, InterGraph offers a comprehensive approach to a graph representation of the intricate spatial organization and connectivity patterns within protein-ligand systems. We introduce interaction spheres that assign varying edge densities, capturing the proximity-based influence of interactions. This approach enables us to capture the characteristics of the interaction network, filtering out the ones that are beyond 9 Å from the ligand since they are not considered relevant or established. Finally, we trained the model using a ligand binding dataset from PDBbind and tested it on a hold-out test set, achieving an RMSE value of 1.34. Our findings have demonstrated the power of the multigraph to encode the importance of close interactions, a factor that is relevant in the context of binding affinity. On average, our model accurately predicts binding affinity values for several protein-ligand complexes and exhibits higher accuracy for hydrolase, lyase, and families of proteins involved in mediating protein-protein interactions. Additionally, the Intergraph method displayed sensitivity to the binding mode when compared to a set of complexes that had undergone redocking calculations.
 
-```{bibtex}
+```bibtex
 @article{mekni2023encoding,
   title={Encoding Protein-Ligand Interactions: Binding Affinity Prediction with Multigraph-based Modeling and Graph Convolutional Network},
   author={Mekni, Nedra and Fooladi, Hosein and Perricone, Ugo and Langer, Thierry},

_publications/2024-04-28-task-hardness.md

@@ -20,7 +20,7 @@ approaches is about the extent to which their performance can benefit from the r
 
 This study introduces a new method for quantifying and predicting the hardness of a bioactivity prediction task based on its relation to the available training tasks. The approach involves the generation of protein and chemical representations and the calculation of distances between the bioactivity prediction task and the available training tasks. In the example of meta-learning, we demonstrate that the proposed task hardness metric is inversely correlated with performance. The metric will be useful in estimating the task specific gain in performance that can be achieved through meta-learning.
 
-```{bibtex}
+```bibtex
 @article{fooladi2024quantifying,
   title={Quantifying the hardness of bioactivity prediction tasks for transfer learning},
   author={Fooladi, Hosein and Hirte, Steffen and Kirchmair, Johannes},

_publications/2025-05-09-pose-sampling.md

@@ -12,7 +12,7 @@ Thi Ngoc Lan Vu, **Hosein Fooladi**, and Johannes Kirchmair
 
 **Abstract**: Physics-based docking methods have long been the cornerstone of structure-based virtual screening (VS). However, the emergence of machine learning (ML)-based docking approaches has opened new possibilities for enhancing VS technologies. In this study, we explore the integration of DiffDock-L, a leading ML-based pose sampling method, into VS workflows by combining it with the Vina, Gnina, and RTMScore scoring functions. We assess this integrated approach in terms of its VS effectiveness, pose sampling quality, and complementarity to traditional physics-based docking methods, such as AutoDock Vina. Our findings from the DUDE-Z benchmark dataset show that DiffDock-L performs competitively in both VS performance and pose sampling in cross-docking settings. In most cases, it generates physically plausible and biologically relevant poses, establishing itself as a viable alternative to physics-based docking algorithms. Additionally, we found that the choice of scoring function significantly influences VS success.
 
-```{bibtex}
+```bibtex
 @article{vu2025integrating,
   title={Integrating Machine Learning-Based Pose Sampling with Established Scoring Functions for Virtual Screening},
   author={Vu, Thi Ngoc Lan and Fooladi, Hosein and Kirchmair, Johannes},

_publications/2025-09-15-ood-evaluation.md

@@ -1,7 +1,7 @@
 ---
 title: "Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data"
 collection: publications
-permalink: /publications/2025-00-15-ood-evaluation
+permalink: /publications/2025-09-15-ood-evaluation
 excerpt: 'Today, machine learning models are employed extensively to predict the physicochemical and biological properties of molecules. Their performance is typically evaluated on in-distribution (ID) data, i.e., data originating from the same distribution as the training data. However, the real-world applications of such models often involve molecules that are more distant from the training data, necessitating the assessment of their performance on out-of-distribution (OOD) data. '
 date: 2025-03-05
 venue: 'Journal of Chemical Information and Modeling'
@@ -12,7 +12,7 @@ citation: 'Fooladi, Hosein, et al. "Evaluating Machine Learning Models for Molec
 
 **Abstract**: Today, machine learning models are employed extensively to predict the physicochemical and biological properties of molecules. Their performance is typically evaluated on in-distribution (ID) data, i.e., data originating from the same distribution as the training data. However, the real-world applications of such models often involve molecules that are more distant from the training data, necessitating the assessment of their performance on out-of-distribution (OOD) data. In this work, we investigate and evaluate the performance of 14 machine learning models, including classical approaches like random forests, as well as graph neural network (GNN) methods, such as message-passing graph neural networks, across eight data sets using ten splitting strategies for OOD data generation. First, we investigate what constitutes OOD data in the molecular domain for bioactivity and ADMET prediction tasks. In contrast to the common point of view, we show that both classical machine learning and GNN models work well (not substantially different from random splitting) on data split based on Bemis-Murcko scaffolds. Splitting based on chemical similarity clustering (UMAP-based clustering using ECFP4 fingerprints) poses the most challenging task for both types of models. Second, we investigate the extent to which ID and OOD performance have a positive linear relationship. If a positive correlation holds, models with the best performance on the ID data can be selected with the promise of having the best performance on OOD data. We show that the strength of this linear relationship is strongly related to how the OOD data is generated, i.e., which splitting strategies are used for generating OOD data. While the correlation between ID and OOD performance for scaffold splitting is strong (Pearson’s r ∼ 0.9), this correlation decreases significantly for all the cluster-based splitting (Pearson’s r ∼ 0.4). Therefore, the relationship can be more nuanced, and a strong positive correlation is not guaranteed for all OOD scenarios. These findings suggest that OOD performance evaluation and model selection should be carefully aligned with the intended application domain.
 
-```{bibtex}
+```bibtex
 @article{fooladi2025evaluating,
   title={Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data},
   author={Fooladi, Hosein and Vu, Thi Ngoc Lan and Mathea, Miriam and Kirchmair, Johannes},