Publications

Quantifying the hardness of bioactivity prediction tasks for transfer learning

Published in Journal of Chemical Information and Modeling, 2024

Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks.

Recommended citation: Fooladi, Hosein, et al. "Quantifying the hardness of bioactivity prediction tasks for transfer learning" Journal of Chemical Information and Modeling (2024) https://chemrxiv.org/engage/chemrxiv/article-details/65b3cafd9138d23161cc5ea4

Encoding Protein-Ligand Interactions: Binding Affinity Prediction with Multigraph-based Modeling and Graph Convolutional Network

Published in ChemRxiv, 2023

Machine learning models are employed to enhance the speed and provide novel insights in drug discovery due to their demonstrated effectiveness in predicting properties of small molecules like pKa, solubility, and binding affinity. These approaches accelerate drug discovery by helping researchers efficiently identify, prioritize, and optimize compounds.

Recommended citation: Mekni, Nedra, et al. "Encoding Protein-Ligand Interactions: Binding Affinity Prediction with Multigraph-based Modeling and Graph Convolutional Network" ChemRxiv (2023) https://chemrxiv.org/engage/chemrxiv/article-details/657c870b66c13817294342d9

Bayesian optimization for ternary complex prediction (BOTCP)

Published in Artificial Intelligence in the Life Sciences, 2023

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 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.

Recommended citation: Rao, Arjun, et al. "Bayesian optimization for ternary complex prediction (BOTCP)." Artificial Intelligence in the Life Sciences 3 (2023): 100072. https://www.sciencedirect.com/science/article/pii/S2667318523000168

Recognizing Arrow Of Time In The Short Stories

Published in ACL, 2019

Recognizing arrow of time in short stories is a challenging task. i.e., given only two paragraphs, determining which comes first and which comes next is a difficult task even for humans. In this paper, we have collected and curated a novel dataset for tackling this challenging task. We have shown that a pre-trained BERT architecture achieves reasonable accuracy on the task, and outperforms RNN-based architectures.

Recommended citation: Hosseini, Fahimeh, Hosein Fooladi, and Mohammad Reza Samsami. "Recognizing Arrow Of Time In The Short Stories." arXiv preprint arXiv:1903.10548 (2019). https://www.aclweb.org/anthology/W19-3606/

Enhanced Waddington Landscape Model with Cell-Cell Communication Can Explain Molecular Mechanisms of Self-Organization

Published in Bioinformatics, 2019

Here, we propose a multicellular mathematical model for pattern formation during in vitro gastrulation of human ESCs. This model enhances the basic principles of Waddington epigenetic landscape with cell-cell communication, in order to enable pattern and tissue formation.

Recommended citation: Hosein, Fooladi. (2019). "Enhanced Waddington Landscape Model with Cell-Cell Communication Can Explain Molecular Mechanisms of Self-Organization." Bioinformatics 1. 1(3). https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz201/5418791?redirectedFrom=fulltext

Optimum Design, Manufacturing and Experiment of a Passive Walking Biped: Effects of Structural Parameters on Efficiency, Stability and Robustness on Uneven Trains

Published in Applied Mechanics and Materials, 2013

In this research, a deployed model of biped that can be built has been considered, and then its walking performance sensitivity such as efficiency, stability and robustness on uneven trains due to variation of structural parameters and their optimum limits have been investigated.

Recommended citation: Hadi, Sadati et al. (2013). "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