14 research outputs found
An exploratory study to assess patterns of influenza- and pneumonia-related mortality among the Italian elderly
No full textOlder adults are at disproportionately high risk of severe influenza-related outcomes and represent the main target of the annual influenza vaccination. The protective effect of seasonal influenza vaccination on the observed mortality indicators is controversial. In this ecological study, spatiotemporal patterns of pneumonia- and influenza-related mortality registered in the Italian elderly over seven (2011–2017) consecutive seasons were explored and the epidemiological association between the observed local pneumonia- and influenza-related mortality and influenza vaccination campaign features were modeled by using both fixed- and random-effects panel regression models. The descriptive spatiotemporal analysis showed a clear North–South gradient, where northern regions tended to report more pneumonia- and influenza-related deaths. After adjustment for potential confounders, it was found that each 1% increase in influenza vaccination coverage rate would be associated (P < .001) with a 1.6–1.9% decrease in pneumonia- and influenza-related mortality. Moreover, each 1% increase in the use of MF59®-adjuvanted trivalent influenza vaccine would be associated (P < .05) with a further 0.4% decrease in pneumonia- and influenza-related mortality. This study supports the increase in annual influenza vaccination in Italy and suggests that a higher level of use of the adjuvanted influenza vaccine in the elderly may be beneficial
Synergies between Quantum Mechanics and Machine Learning for Advancing Pharmaceutical Research
No full textThe drug development process is resource-intensive, often costing billions and taking over a decade, yet many candidates still fail in late-stage trials. This thesis addresses key bottlenecks in early-stage drug discovery—such as navigating chemical space, modeling molecular interactions, and predicting biological properties—by integrating quantum chemistry and machine learning to develop more accurate and scalable computational methodologies. The analysis of the Aquamarine (AQM) dataset, designed to capture the interplay between molecular conformations, solvation effects, and non-covalent interactions, is presented as a key milestone for future machine learning models dealing with solvation effects for relevant molecules in medicinal chemistry. The results of the analysis reveal that many-body dispersion effects and implicit solvation significantly influence molecular geometries, reinforcing the necessity of accurate modeling for reliable predictions in biological environments. In a similar direction, the thesis introduces also a photonic quantum simulation framework for studying full Coulomb interactions between quantum Drude oscillators as a way to study dis- persion beyond the dipole approximation typical of current models. This study uncovers non-trivial quantum effects, including the formation of entangled Schrödinger cat states during binding and offering insights into the fundamental nature of dispersion interactions. Moving from fundamental problems to more practical applications, the Quantum Inverse Mapping (QIM) framework is introduced to establish a direct, differentiable connection between quantum mechanical properties and molecular structures. This enables multi-objective molecular design and generation of transition path initializations, demonstrating its utility in navigating chemical spaces for different tasks. Finally, the thesis explores the role of quantum chem- istry data in enhancing deep learning models for ADMET property modeling. A systematic study on Graph Transformer reveals that pretraining on atom-level quantum properties improves the model’s representation, leading to superior performance. Collectively, these contributions bridge quantum chemistry with machine learning to address key challenges in molecular exploration, electronic structure calculation, and biological property modeling, advancing computational methodologies for rational drug discovery
Beyond the Concepts of Elder and Marginal in DCD Liver Transplantation: A Prospective Observational Matched-Cohort Study in the Italian Clinical Setting
No full textDonation after circulatory determination of death (DCD) is a valuable strategy to increase the availability of grafts for liver transplantation (LT). As the average age of populations rises, the donor pool is likely to be affected by a potential increase in DCD donor age in the near future. We conducted a prospective cohort study to evaluate post-transplantation outcomes in recipients of grafts from elderly DCD donors compared with younger DCD donors, and elderly donors after brainstem determination of death (DBD). From August 2020 to May 2022, consecutive recipients of deceased donor liver-only transplants were enrolled in the study. DCD recipients were propensity score matched 1:3 to DBD recipients. One-hundred fifty-seven patients were included, 26 of whom (16.6%) were transplanted with a DCD liver graft. After propensity score matching and stratification, three groups were obtained: 15 recipients of DCD donors & GE;75 years, 11 recipients of DCD donors <75 years, and 28 recipients of DBD donors & GE;75 years. Short-term outcomes, as well as 12 months graft survival rates (93.3%, 100%, and 89.3% respectively), were comparable among the groups. LT involving grafts retrieved from very elderly DCD donors was feasible and safe in an experienced high-volume center, with outcomes comparable to LTs from younger DCD donors and age-matched DBD donors
Learning Feedback Control Strategies for Quantum Metrology
No full textWe consider the problem of frequency estimation for a single bosonic field evolving under a squeezing Hamiltonian and continuously monitored via homodyne detection. In particular, we exploit reinforcement learning techniques to devise feedback control strategies achieving increased estimation precision. We show that the feedback control determined by the neural network greatly surpasses in the long-time limit the performances of both the "no-control" strategy and the standard "open-loop control" strategy, which we considered as benchmarks. We indeed observe how the devised strategy is able to optimize the nontrivial estimation problem by preparing a large fraction of trajectories corresponding to more sensitive quantum conditional states
Modeling noncovalent interatomic interactions on a photonic quantum computer
No full texteditorial reviewedNoncovalent interactions are a key ingredient to determine the structure, stability, and dynamics of materials, molecules, and biological complexes. However, accurately capturing these interactions is a complex quantum many-body problem, with no efficient solution available on classical computers. A widely used model to accurately and efficiently model noncovalent interactions is the Coulomb-coupled quantum Drude oscillator (cQDO) many-body Hamiltonian, for which no exact solution is known. We show that the cQDO model lends itself naturally to simulation on a photonic quantum computer, and we calculate the binding energy curve of diatomic systems by leveraging Xanadu's strawberry fields photonics library. Our study substantially extends the applicability of quantum computing to atomistic modeling by showing a proof-of-concept application to noncovalent interactions, beyond the standard electronic-structure problem of small molecules. Remarkably, we find that two coupled bosonic QDOs exhibit a stable bond. In addition, our study suggests efficient functional forms for cQDO wave functions that can be optimized on classical computers, and capture the bonded-to-noncovalent transition for increasing interatomic distances
Enabling Inverse Design in Chemical Compound Space: Mapping Quantum Properties to Structures for Small Organic Molecules
No full textComputer-driven molecular design combines the principles of chemistry,
physics, and artificial intelligence to identify novel chemical compounds and
materials with desired properties for a specific application. In particular,
quantum-mechanical (QM) methods combined with machine learning (ML) techniques
have accelerated the estimation of accurate molecular properties, providing a
direct mapping from 3D molecular structures to their properties. However, the
development of reliable and efficient methodologies to enable \emph{inverse
mapping} in chemical space is a long-standing challenge that has not been
accomplished yet. Here, we address this challenge by demonstrating the
possibility of parametrizing a given chemical space with a finite set of
extensive and intensive QM properties. In doing so, we develop a
proof-of-concept implementation that combines a Variational Auto-Encoder (VAE)
trained on molecular structures with a property encoder designed to learn the
latent representation from a set of QM properties. The result of this joint
architecture is a common latent space representation for both structures and
properties, which enables property-to-structure mapping for small drug-like
molecules contained in the QM7-X dataset. We illustrate the capabilities of our
approach by conditional generation of \emph{de novo} molecular structures with
targeted properties, transition path interpolation for chemical reactions as
well as insights into property-structure relationships. Our findings thus
provide a proof-of-principle demonstration aiming to enable the inverse
property-to-structure design in diverse chemical spaces.Comment: 17 pages, 8 figures, 1 tabl
Inverse mapping of quantum properties to structures for chemical space of small organic molecules
No full textAbstract Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify chemical compounds with tailored properties. While quantum-mechanical (QM) methods, coupled with machine learning, already offer a direct mapping from 3D molecular structures to their properties, effective methodologies for the inverse mapping in chemical space remain elusive. We address this challenge by demonstrating the possibility of parametrizing a chemical space with a finite set of QM properties. Our proof-of-concept implementation achieves an approximate property-to-structure mapping, the QIM model (which stands for “Quantum Inverse Mapping”), by forcing a variational auto-encoder with a property encoder to obtain a common internal representation for both structures and properties. After validating this mapping for small drug-like molecules, we illustrate its capabilities with an explainability study as well as by the generation of de novo molecular structures with targeted properties and transition pathways between conformational isomers. Our findings thus provide a proof-of-principle demonstration aiming to enable the inverse property-to-structure design in diverse chemical spaces
Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules
No full text<p>Open challenges in computational drug design include the understanding and accurate description of solvent effects as well as collective dispersion interactions for realistic drug-like molecules. Both interactions profoundly influence the conformational stability of drug molecules and, consequently, the determination of other important quantum-mechanical (QM) observables. In this context, we here introduce the Aquamarine (AQM) dataset -- an extensive QM dataset that contains the structural and electronic information -- of 59,786 low-and high-energy conformers of 1,653 molecules containing up to 54 non-hydrogen atoms (including C, N, O, F, P, S and Cl). To gain insights into the solvent effects, we have carried out QM calculations of structures and properties in gas phase and in an aqueous solution modeled with implicit solvent. AQM contains over 40 global (molecular) and local (atom-in-a-molecule) physicochemical properties (including ground-state and response properties) per molecular structure computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD supplemented with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By treating both molecule-solvent and dispersion interactions, the AQM dataset can help understand the impact of both interactions in structure-property and property-property relationships of realistic drug-like molecules. Therefore, we propose the AQM dataset as a benchmark for current state-of-the-art machine learning methods for property prediction as well as for the <em>de novo</em> generation of large and flexible (solvated) molecules with pharmaceutical and biological relevance.</p>
Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling
No full textAbstract We evaluate the impact of pretraining Graph Transformer architectures on atom-level quantum-mechanical features for the modeling of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. We compare this pretraining strategy with two others: one based on molecular quantum properties (specifically the HOMO-LUMO gap) and one using a self-supervised atom masking technique. After fine-tuning on Therapeutic Data Commons ADMET datasets, we evaluate the performance improvement in the different models observing that models pretrained with atomic quantum mechanical properties produce in general better results. We then analyze the latent representations and observe that the supervised strategies preserve the pretraining information after fine-tuning and that different pretrainings produce different trends in latent expressivity across layers. Furthermore, we find that models pretrained on atomic quantum mechanical properties capture more low-frequency Laplacian eigenmodes of the input graph via the attention weights and produce better representations of atomic environments within the molecule. Application of the analysis to a much larger non-public dataset for microsomal clearance illustrates generalizability of the studied indicators. In this case the performances of the models are in accordance with the representation analysis and highlight, especially for the case of masking pretraining and atom-level quantum property pretraining, how model types with similar performance on public benchmarks can have different performances on large scale pharmaceutical data. Scientific contribution We systematically compared three different data type/methodologies for pretraining molecular Graphormer with the purpose of modeling ADMET properties as downstream tasks. The learned representations from differently pretrained models were analyzed in addition to comparison of downstream task performances that have been typically reported in similar works. Such examination methodologies, including a newly introduced analysis of Graphormer’s Attention Rollout Matrix, can guide pretraining strategy selection, as corroborated by a performance evaluation on a larger internal dataset
Author Correction: Hypothermic Oxygenated New Machine Perfusion System in Liver and Kidney Transplantation of Extended Criteria Donors: First Italian Clinical Trial (Scientific Reports, (2020), 10, 1, (6063), 10.1038/s41598-020-62979-9)
No full textThis Article contains an error in the order of the Figures. Figures 1, 2 and 3 were published as Figures 2, 3 and 1 respectively. The correct Figures appear below as Figures 1, 2 and 3. The Figure legends are correct
