Luna Pianesi

Luna Pianesi

she/her

PhD student in Bioinformatics

Publications

Journal Articles

A unified computational framework for the integration of AI models in structure-based drug design

Published in GEM workshop, ICLR 2026, 2026

Abstract: The rapid proliferation of sophisticated computational models for drug discovery has created unprecedented opportunities for innovation, yet the field lacks comprehensive frameworks to systematically integrate these diverse tools into coherent workflows. Although these models demonstrate remarkable individual capabilities, researchers are forced to navigate fragmented toolsets requiring extensive computational expertise, limiting the practical impact of these advances and creating an accessibility problem. In this paper, we present a comprehensive and modular computational drug discovery pipeline that provides the first systematic framework for integrating diverse state-of-the-art models into an accessible unified drug discovery workflow. The workflow is based on the integration of state-of-the-art generative and docking models, with a special focus on ensuring the synthetic accessibility and real world scenario plausibility of the proposed molecules.

Recommended citation: Pianesi, L. and Schönhuth, A. (2026). "A Unified Computational Framework for the Integration of AI Models in Structure-Based Drug Design." GEM workshop, ICLR.
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Considerations in the search for epistasis

Published in Genome Biology, 2024

Abstract: Epistasis refers to changes in the effect on phenotype of a unit of genetic information, such as a single nucleotide polymorphism or a gene, dependent on the context of other genetic units. Such interactions are both biologically plausible and good candidates to explain observations which are not fully explained by an additive heritability model. However, the search for epistasis has so far largely failed to recover this missing heritability. We identify key challenges and propose that future works need to leverage idealized systems, known biology and even previously identified epistatic interactions, in order to guide the search for new interactions.

Recommended citation: Balvert, M., Cooper-Knock, J., Stamp, J., Byrne, R.P., Mourragui, S., van Gils, J., Benonisdottir, S., Schlüter, J., Kenna, K., Abeln, S., Iacoangeli, A., Daub, J.T., Browning, B.L., Taş, G., Hu, J., Wang, Y., Alhathli, E., Harvey, C., Pianesi, L., Schulte, S.C., González-Domínguez J., Garrisson, E., Lorentz workshop on epistasis, Snyder, M.P., Schönhuth, A., Sng, L.M.F. and Twine, N.A. (2024). "Considerations in the search for epistasis." Genome Biology. 25(296).
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Searching for a source of difference in undirected graphical models for count data: an empirical study

Published in Società Italiana di Statistica (SIS), 2021

Abstract: A study is presented for exploring the possibility of applying the Source set approach, developed under the assumption of normality, to count data, after data transformation. Some explanations about the source set approach, data transformations and the simulation setting are provided. The suggestion is given that the deviance-based or quantile randomized residuals could provide a better basis for data transformation when coupled with source set analysis, along with standard trasformations such as log transformation or square root transformation.

Recommended citation: Agostinis, F., Chiogna, M., Djordjilovic, V., Pianesi, L. and Romualdi, C. (2021). "Paper Title Number 1." Book of short papers. SIS - Società Italiana di Statistica, 689-694.
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