Francesca Grisoni

Last Name

Grisoni

First Name

Francesca

ORCID

0000-0001-8552-6615

Show all metadata

Search Results

Publications 1 - 10 of 23

Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators
Merk, Daniel; Grisoni, Francesca; Friedrich, Lukas; et al. (2018)
Communications Chemistry
Generating customized compound libraries for drug discovery with machine intelligence
Moret, Michael; Friedrich, Lukas; Grisoni, Francesca; et al. (2019)
ChemRxiv
Generative machine learning models sample drug-like molecules from chemical space without the need for explicit design rules. A deep learning framework for customized compound library generation is presented, aiming to enrich and expand the pharmacologically relevant chemical space with new molecular entities 'on demand'. This de novo design approach was used to generate molecules that combine features from bioactive synthetic compounds and natural products, which are a primary source of inspiration for drug discovery. The results show that the data-driven machine intelligence acquires implicit chemical knwoledge and generates novel molecules with bespoke properties and structural diversity. The method is available as an open-access tool for medicinal and bioorganic chemistry.
Beam-Search zum automatisierten Entwurf und Scoring neuer ROR-Liganden mithilfe maschineller Intelligenz
Moret, Michael; Helmstädter, Moritz; Grisoni, Francesca; et al. (2021)
Angewandte Chemie. International Edition
Chemische Sprachmodelle ermöglichen ein De-novo-Wirkstoff-Design ohne explizite chemische Konstruktionsregeln. Während solche Modelle angewendet wurden, um neuartige Verbindungen mit angestrebter biologischer Aktivität zu generieren, bleibt die tatsächliche Priorisierung und Auswahl der vielversprechendsten Molekülentwürfe (“Designs”) eine Herausforderung. Wir haben hier die von chemischen Sprachmodellen gelernten Wahrscheinlichkeiten mithilfe des Beam-Search-Algorithmus als Modell-intrinsische Technik für das Moleküldesign und die Bewertung der Designs (“Scoring”) genutzt. Die prospektive Anwendung dieser Methode führte zu neuartigen inversen Agonisten der Retinoid-related-Orphan-Rezeptoren (RORs). Jedes Design war in drei Reaktionsschritten synthetisierbar und zeigte eine niedrig-mikromolare bis nanomolare Potenz gegenüber RORγ. Als Modell-intrinsische Technik eliminiert das Beam-Search-Sampling die strikte Notwendigkeit externer Molekül-Scoring-Funktionen und erweitert damit die Anwendbarkeit generativer künstlicher Intelligenz in der datengetriebenen Wirkstoffforschung.
Leveraging molecular structure and bioactivity with chemical language models for de novo drug design
Moret, Michael; Pachon-Angona, Irene; Cotos, Leandro; et al. (2023)
Nature Communications
Generative chemical language models (CLMs) can be used for de novo molecular structure generation by learning from a textual representation of molecules. Here, we show that hybrid CLMs can additionally leverage the bioactivity information available for the training compounds. To computationally design ligands of phosphoinositide 3-kinase gamma (PI3Kγ), a collection of virtual molecules was created with a generative CLM. This virtual compound library was refined using a CLM-based classifier for bioactivity prediction. This second hybrid CLM was pretrained with patented molecular structures and fine-tuned with known PI3Kγ ligands. Several of the computer-generated molecular designs were commercially available, enabling fast prescreening and preliminary experimental validation. A new PI3Kγ ligand with sub-micromolar activity was identified, highlighting the method’s scaffold-hopping potential. Chemical synthesis and biochemical testing of two of the top-ranked de novo designed molecules and their derivatives corroborated the model’s ability to generate PI3Kγ ligands with medium to low nanomolar activity for hit-to-lead expansion. The most potent compounds led to pronounced inhibition of PI3K-dependent Akt phosphorylation in a medulloblastoma cell model, demonstrating efficacy of PI3Kγ ligands in PI3K/Akt pathway repression in human tumor cells. The results positively advocate hybrid CLMs for virtual compound screening and activity-focused molecular design.
Artificial intelligence in drug discovery: recent advances and future perspectives
Jiménez-Luna, José; Grisoni, Francesca; Weskamp, Nils; et al. (2021)
Expert Opinion on Drug Discovery
Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry. Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery. Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
Molecular Design With Long Short-Term Memory Networks
Grisoni, Francesca; Schneider, Gisbert (2019)
Journal of Computer Aided Chemistry
Verification of Chromatographic Profile of Primary Essential Oil of Pinus sylvestris L. Combined with Chemometric Analysis
Allenspach, Martina; Valder, Claudia; Flamm, Daniela; et al. (2020)
Molecules
Chromatographic profiles of primary essential oils (EO) deliver valuable authentic information about composition and compound pattern. Primary EOs obtained from Pinus sylvestris L. (PS) from different global origins were analyzed using gas chromatography coupled to a flame ionization detector (GC-FID) and identified by GC hyphenated to mass spectrometer (GC-MS). A primary EO of PS was characterized by a distinct sesquiterpene pattern followed by a diterpene profile containing diterpenoids of the labdane, pimarane or abietane type. Based on their sesquiterpene compound patterns, primary EOs of PS were separated into their geographical origin using component analysis. Furthermore, differentiation of closely related pine EOs by partial least square discriminant analysis proved the existence of a primary EO of PS. The developed and validated PLS-DA model is suitable as a screening tool to assess the correct chemotaxonomic identification of a primary pine EOs as it classified all pine EOs correctly.
Perplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models
Moret, Michael; Grisoni, Francesca; Katzberger, Paul; et al. (2022)
Journal of Chemical Information and Modeling
Chemical language models (CLMs) can be employed to design molecules with desired properties. CLMs generate new chemical structures in the form of textual representations, such as the simplified molecular input line entry system (SMILES) strings. However, the quality of these de novo generated molecules is difficult to assess a priori. In this study, we apply the perplexity metric to determine the degree to which the molecules generated by a CLM match the desired design objectives. This model-intrinsic score allows identifying and ranking the most promising molecular designs based on the probabilities learned by the CLM. Using perplexity to compare "greedy" (beam search) with "explorative" (multinomial sampling) methods for SMILES generation, certain advantages of multinomial sampling become apparent. Additionally, perplexity scoring is performed to identify undesired model biases introduced during model training and allows the development of a new ranking system to remove those undesired biases.
Q&A with Professor Gisbert Schneider and Dr Francesca Grisoni
Schneider, Gisbert; Grisoni, Francesca (2019)
Communications Chemistry
Drug discovery with explainable artificial intelligence
Jiménez-Luna, José; Grisoni, Francesca; Schneider, Gisbert (2020)
Nature Machine Intelligence
Deep learning bears promise for drug discovery, including advanced image analysis, prediction of molecular structure and function, and automated generation of innovative chemical entities with bespoke properties. Despite the growing number of successful prospective applications, the underlying mathematical models often remain elusive to interpretation by the human mind. There is a demand for ‘explainable’ deep learning methods to address the need for a new narrative of the machine language of the molecular sciences. This Review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and forecasts future opportunities, potential applications as well as several remaining challenges. We also hope it encourages additional efforts towards the development and acceptance of explainable artificial intelligence techniques. © 2020, Springer Nature Limited

Publications 1 - 10 of 23

Francesca Grisoni

Last Name

First Name

ORCID

Organisational unit

Filters

Search Results