Update publications: Fix the bibtex entries in the publication section

Author: HFooladi

_publications/2023-12-01-botcp.md

@@ -16,18 +16,18 @@ citation: 'Rao, Arjun, et al. "Bayesian optimization for ternary complex predict
 Rao, Arjun, Tin M. Tunjic, Michael Brunsteiner, Michael Müller, **Hosein Fooladi**, Chiara Gasbarri, and Noah Weber
 
 **Abstract**: Proximity-inducing compounds (PICs) are an emergent drug technology through which a protein of interest (POI), often a drug target, is
- brought into the vicinity of a second protein which modifies the POI’s function, abundance or localisation, giving rise to a therapeutic effect. 
- One of the best-known examples for such compounds are heterobifunctional molecules known as proteolysis targeting chimeras (PROTACs). 
- PROTACs reduce the abundance of the target protein by establishing proximity to an E3 ligase which labels the protein for degradation via 
+ brought into the vicinity of a second protein which modifies the POI’s function, abundance or localisation, giving rise to a therapeutic effect.
+ One of the best-known examples for such compounds are heterobifunctional molecules known as proteolysis targeting chimeras (PROTACs).
+ PROTACs reduce the abundance of the target protein by establishing proximity to an E3 ligase which labels the protein for degradation via
  the ubiquitin-proteasomal pathway. Design of PROTACs in silico requires the computational prediction of the ternary complex consisting of POI, PROTAC molecule, and the E3 ligase.
 
-We present a novel machine learning-based method for predicting PROTAC-mediated ternary complex structures using Bayesian optimization. 
-We show how a fitness score combining an estimation of protein-protein interactions with PROTAC conformation energy calculations enables 
-the sample-efficient exploration of candidate structures. Furthermore, our method presents two novel scores for filtering and reranking which 
-take PROTAC stability (Autodock-Vina based PROTAC stability score) and protein interaction restraints (the TCP-AIR score) into account. 
-We evaluate our method using DockQ scores on a number of available ternary complex structures (including previously unevaluated cases) and 
-demonstrate that even with a clustering that requires members to have a high similarity, i.e., with smaller clusters, we can assign high ranks to 
-those clusters that contain poses close to the experimentally determined native structure of the ternary complexes. 
+We present a novel machine learning-based method for predicting PROTAC-mediated ternary complex structures using Bayesian optimization.
+We show how a fitness score combining an estimation of protein-protein interactions with PROTAC conformation energy calculations enables
+the sample-efficient exploration of candidate structures. Furthermore, our method presents two novel scores for filtering and reranking which
+take PROTAC stability (Autodock-Vina based PROTAC stability score) and protein interaction restraints (the TCP-AIR score) into account.
+We evaluate our method using DockQ scores on a number of available ternary complex structures (including previously unevaluated cases) and
+demonstrate that even with a clustering that requires members to have a high similarity, i.e., with smaller clusters, we can assign high ranks to
+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

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

@@ -3,7 +3,7 @@ title: "Evaluating Machine Learning Models for Molecular Property Prediction: Pe
 collection: publications
 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
+date: 2025-09-15
 venue: 'Journal of Chemical Information and Modeling'
 paperurl: 'https://doi.org/10.1021/acs.jcim.5c00475'
 citation: 'Fooladi, Hosein, et al. "Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data." Journal of Chemical Information and Modeling 65.19 (2025): 9871-9891.'