A Drug Administration Decision Support System

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Cyclic peptides are fascinating molecules abundantly found in nature and exploited as molecular format for drug development as well as other applications, ranging from research tools to food additives. Advances in peptide technologies made over many years ...

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learni ...

In the domain of computational structural biology, predicting protein interactions based on molecular structure remains a pivotal challenge. This thesis delves into this challenge through a series of interconnected studies. The first chapter introduces th ...

Retinoblastoma, while relatively rare, stands as the most prevalent intraocular cancer. In Switzerland, the survival rate approaches 100%, but it drops to less than 50% in low-income countries. The current treatment options for retinoblastoma rely on a lim ...

Retinoblastoma is a rare childhood cancer of the eye. Of the small number of drugs are used to treat retinoblastoma, all have been repurposed from drugs developed for other conditions. In order to find drugs or drug combinations better suited to the improv ...

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomark ...

Chemical space maps help visualize similarities within molecular sets. However, there are many different molecular similarity measures resulting in a confusing number of possible comparisons. To overcome this limitation, we exploit the fact that tools desi ...

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to e ...

Retinoblastoma is the most common pediatric eye cancer. It is currently treated with a limited number of drugs, adapted from other pediatric cancer treatments. Drug toxicity and relapse of the disease warrant new therapeutic strategies for these young pati ...

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised le ...

The goal of retinoblastoma (RB) treatment is to save the child's life, eyes and functional vision, with that order of priority. The management of RB is complex and involves strategically chosen methods of surgical enucleation, external beam radiotherapy, i ...

The determination of intracellular drug concentrations can provide a better understanding of the drug function and efficacy. Ideally, this should be performed nondestructively, with no modification of either the drug or the target, and with the capability ...

Intercurrent (post-treatment) events occur frequently in randomized trials, and investigators often express interest in treatment effects that suitably take account of these events. Contrasts that naively condition on intercurrent events do not have a stra ...

Synthetic biology aims to engineer cells as miniature biological devices to sense, process, and respond to exogenous stimuli. Protein switches are designed to sense and respond to various molecular queues in a fast and specific manner, which fits the requi ...

Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects. But, discrete domains are (I) not naturally amenable to gradient-based optimization, and (II) incompatible with deep lear ...

Supervised and unsupervised kernel-based algorithms widely used in the physical sciences depend upon the notion of similarity. Their reliance on pre-defined distance metrics-e.g. the Euclidean or Manhattan distance-are problematic especially when used in c ...

Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel str ...

Peptides have a number of attractive properties that make them an interesting modality for drug development, including their ability to bind challenging targets, their high target specificity, and their non-toxic metabolic products. However, a major limita ...

The discovery of a drug requires over a decade of intensive research and financial investments – and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied ...

Jet injection devices have been studied and developed for transdermal drug delivery to avoid the use of needles. Such contact-less devices provide several advantages such as better dose control, lateral and depth localisation, lower collateral damage than ...

A Drug Administration Decision Support System

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Cyclic Peptides for Drug Development

Computational drug development for membrane protein targets

Predicting protein interactions using geometric deep learning on protein surfaces

Optimised three-dimensional in vitro retinoblastoma models tailored for drug discovery and clinical translatability

The predictive capacity of in vitro preclinical models to evaluate drugs for the treatment of retinoblastoma

Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy

Alchemical analysis of FDA approved drugs

Artificial intelligence for natural product drug discovery

Translational screening platform to evaluate chemotherapy in combination with focal therapy for retinoblastoma

A structured prediction approach for robot imitation learning

Repurposed drug combination via high-throughput screening and dual drug delivery system development for the treatment of retinoblastoma

In-Cell Quantification of Drugs by Magic-Angle Spinning Dynamic Nuclear Polarization NMR

Translating questions to estimands in randomized clinical trials with intercurrent events

Rational design of protein switches: applications in synthetic biology and cancer immunotherapy

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Metric learning for kernel ridge regression: assessment of molecular similarity

Deep learning approaches for conformational flexibility and switching properties in protein design

Towards the Development of Orally Available Peptide Therapeutics

NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

Laser-induced liquid micro-jets for drug delivery