Francesca Milletti

3.6k total citations · 2 hit papers
32 papers, 2.2k citations indexed

About

Francesca Milletti is a scholar working on Oncology, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Francesca Milletti has authored 32 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Oncology, 13 papers in Molecular Biology and 10 papers in Computational Theory and Mathematics. Recurrent topics in Francesca Milletti's work include CAR-T cell therapy research (12 papers), Computational Drug Discovery Methods (10 papers) and Free Radicals and Antioxidants (5 papers). Francesca Milletti is often cited by papers focused on CAR-T cell therapy research (12 papers), Computational Drug Discovery Methods (10 papers) and Free Radicals and Antioxidants (5 papers). Francesca Milletti collaborates with scholars based in United States, Switzerland and Italy. Francesca Milletti's co-authors include Loriano Storchi, Gabriele Cruciani, Gianluca Sforna, Anna Vulpetti, Laura Goracci, Simon Cross, Sattva S. Neelapu, Ernest L. Mehler, Jana K. Shen and Sarah Williams and has published in prestigious journals such as Angewandte Chemie International Edition, Nature Medicine and Journal of Clinical Oncology.

In The Last Decade

Francesca Milletti

31 papers receiving 2.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Cell-penetrating peptides: classes, origin, and current landscape

2012 658 citations Francesca Milletti Drug Discovery Today profile →
Axicabtagene ciloleucel as first-line therapy in high-risk large B-cell lymphoma: the phase 2 ZUMA-12 trial

2022 186 citations Sattva S. Neelapu, Michael Dickinson et al. Nature Medicine profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Francesca Milletti United States	18	1.4k	511	415	283	183	32	2.2k
Vikram Khipple Mulligan United States	20	2.5k 1.8×	183 0.4×	214 0.5×	171 0.6×	167 0.9×	32	3.0k
Chittaranjan Das United States	28	1.8k 1.3×	349 0.7×	129 0.3×	278 1.0×	159 0.9×	83	2.5k
Lorenz M. Mayr Switzerland	28	2.3k 1.7×	434 0.8×	309 0.7×	247 0.9×	207 1.1×	51	3.1k
Djordje Müsil Germany	27	1.8k 1.3×	564 1.1×	302 0.7×	317 1.1×	114 0.6×	50	3.0k
Grzegorz M. Popowicz Germany	33	2.3k 1.7×	822 1.6×	217 0.5×	581 2.1×	132 0.7×	101	3.3k
Robert S. McDowell United States	28	2.8k 2.0×	563 1.1×	486 1.2×	969 3.4×	192 1.0×	44	4.3k
Marina Ramı́rez-Alvarado United States	38	3.9k 2.8×	577 1.1×	125 0.3×	186 0.7×	247 1.3×	105	4.6k
Andrew C. Braisted United States	14	1.9k 1.4×	341 0.7×	380 0.9×	699 2.5×	55 0.3×	17	2.5k
Oliver Koch Germany	21	1.4k 1.0×	205 0.4×	386 0.9×	576 2.0×	32 0.2×	89	1.9k
Gregory H. Bird United States	32	3.5k 2.5×	701 1.4×	95 0.2×	701 2.5×	125 0.7×	59	4.1k

Countries citing papers authored by Francesca Milletti

Since Specialization

Citations

This map shows the geographic impact of Francesca Milletti's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Francesca Milletti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesca Milletti more than expected).

Fields of papers citing papers by Francesca Milletti

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Francesca Milletti. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Francesca Milletti. The network helps show where Francesca Milletti may publish in the future.

Co-authorship network of co-authors of Francesca Milletti

This figure shows the co-authorship network connecting the top 25 collaborators of Francesca Milletti. A scholar is included among the top collaborators of Francesca Milletti based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Francesca Milletti. Francesca Milletti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Filosto, Simone, Manoj Chiney, Francesca Milletti, et al.. (2024). Investigating the Influence of Covariates on Axicabtagene Ciloleucel (axi-cel) Kinetics in Patients with Non-Hodgkin’s Lymphoma. Clinical Pharmacokinetics. 63(9). 1283–1299. 2 indexed citations

Neelapu, Sattva S., Michael Dickinson, Javier Muñoz, et al.. (2022). Axicabtagene ciloleucel as first-line therapy in high-risk large B-cell lymphoma: the phase 2 ZUMA-12 trial. Nature Medicine. 28(4). 735–742. 186 indexed citations breakdown →

Neelapu, Sattva S., Michael Dickinson, Javier Muñoz, et al.. (2021). Primary Analysis of ZUMA-12: A Phase 2 Study of Axicabtagene Ciloleucel (Axi-Cel) As First-Line Therapy in Patients with High-Risk Large B-Cell Lymphoma (LBCL). Blood. 138(Supplement 1). 739–739. 15 indexed citations

Budka, Justin A., Justin Chou, Vicki Plaks, et al.. (2021). Abstract CT166: Pretreatment (PreTx) immune cell phenotypes in peripheral blood associated with the tumor immune contexture, product attributes, and durable clinical efficacy in patients with large B-cell lymphoma (LBCL) treated with axicabtagene ciloleucel (axi-cel). Cancer Research. 81(13_Supplement). CT166–CT166. 1 indexed citations

Neelapu, Sattva S., Michael Dickinson, Matthew L. Ulrickson, et al.. (2020). Interim Analysis of ZUMA-12: A Phase 2 Study of Axicabtagene Ciloleucel (Axi-Cel) as First-Line Therapy in Patients (Pts) With High-Risk Large B Cell Lymphoma (LBCL). Blood. 136(Supplement 1). 49–49. 34 indexed citations

Augert, Arnaud, Emily Eastwood, Ali H. Ibrahim, et al.. (2019). Targeting NOTCH activation in small cell lung cancer through LSD1 inhibition. Science Signaling. 12(567). 154 indexed citations

Pradier, Melanie F., Bernhard Reis, Lori Jukofsky, et al.. (2019). Case-control Indian buffet process identifies biomarkers of response to Codrituzumab. BMC Cancer. 19(1). 278–278. 3 indexed citations

Neelapu, Sattva S., John M. Rossi, Caron A. Jacobson, et al.. (2019). CD19-Loss with Preservation of Other B Cell Lineage Features in Patients with Large B Cell Lymphoma Who Relapsed Post-Axi-Cel. Blood. 134(Supplement_1). 203–203. 51 indexed citations

Neelapu, Sattva S., Mohamed A. Kharfan‐Dabaja, Olalekan O. Oluwole, et al.. (2019). A Phase 2, Open-Label, Multicenter Study Evaluating the Safety and Efficacy of Axicabtagene Ciloleucel in Combination with Either Rituximab or Lenalidomide in Patients with Refractory Large B-Cell Lymphoma (ZUMA-14). Blood. 134(Supplement_1). 4093–4093. 7 indexed citations

10.

Schmidt, Samuel, et al.. (2016). Identification of Short Hydrophobic Cell-Penetrating Peptides for Cytosolic Peptide Delivery by Rational Design. Bioconjugate Chemistry. 28(2). 382–389. 35 indexed citations

11.

Schmidt, Samuel, Merel J.W. Adjobo-Hermans, Rike Wallbrecher, et al.. (2015). Detecting Cytosolic Peptide Delivery with the GFP Complementation Assay in the Low Micromolar Range. Angewandte Chemie International Edition. 54(50). 15105–15108. 38 indexed citations

12.

Milletti, Francesca, et al.. (2014). Quantifying the probability of clinical trial success from scientific articles. Drug Discovery Today. 19(10). 1514–1517. 4 indexed citations

13.

Milletti, Francesca. (2012). Cell-penetrating peptides: classes, origin, and current landscape. Drug Discovery Today. 17(15-16). 850–860. 658 indexed citations breakdown →

14.

Milletti, Francesca & Johannes C. Hermann. (2012). Targeted Kinase Selectivity from Kinase Profiling Data. ACS Medicinal Chemistry Letters. 3(5). 383–386. 20 indexed citations

15.

Alexov, Emil, Ernest L. Mehler, Nathan Baker, et al.. (2011). Progress in the prediction of pK_avalues in proteins. Proteins Structure Function and Bioinformatics. 79(12). 3260–3275. 221 indexed citations

16.

Milletti, Francesca & Anna Vulpetti. (2010). Predicting Polypharmacology by Binding Site Similarity: From Kinases to the Protein Universe. Journal of Chemical Information and Modeling. 50(8). 1418–1431. 80 indexed citations

17.

Milletti, Francesca, Loriano Storchi, Laura Goracci, et al.. (2010). Extending pKa prediction accuracy: High-throughput pKa measurements to understand pKa modulation of new chemical series. European Journal of Medicinal Chemistry. 45(9). 4270–4279. 94 indexed citations

18.

Cruciani, Gabriele, Francesca Milletti, Loriano Storchi, Gianluca Sforna, & Laura Goracci. (2009). In silico pK_a Prediction and ADME Profiling. Chemistry & Biodiversity. 6(11). 1812–1821. 66 indexed citations

19.

Milletti, Francesca, Loriano Storchi, Gianluca Sforna, Simon Cross, & Gabriele Cruciani. (2009). Tautomer Enumeration and Stability Prediction for Virtual Screening on Large Chemical Databases. Journal of Chemical Information and Modeling. 49(1). 68–75. 107 indexed citations

20.

Milletti, Francesca, Loriano Storchi, & Gabriele Cruciani. (2009). Predicting protein pK_a by environment similarity. Proteins Structure Function and Bioinformatics. 76(2). 484–495. 18 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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