Publications related to Transformers and Large Language Models for Chemistry and Drug Discovery

Related publications (20)

High-Resolution Predictions of the Fuel and Cladding Temperatures for the 3D PWR Core with Artificial Neural Networks trained on CTF

High resolution multi-physics core solvers predict safety parameters on a local scale with high accuracy while simulating local heterogeneities and their consequences. To achieve this, large computational resources are needed which renders the extensive us ...

American Nuclear Society2025

Identifying Potent Compounds Using Pairwise Consensus Methods

Horst Arthur Vogel

Molecular docking is a widely used method within the in silico compound screening process of modern drug discovery. The accuracy of this method for predicting high-affinity small-molecule binders for a target protein from a large chemical library can be su ...

American Chemical Society (ACS)2025

Interactive symbolic regression with co-design mechanism through offline reinforcement learning

Wenqing Zhou, Olga Fink, Michele Viscione

Symbolic Regression holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possible expressions poses significant challenges for previous online search methods an ...

Springer Science and Business Media LLC2025

Combining physics-based and data-driven modeling for building energy systems

Leandro Von Krannichfeldt, Olga Fink

Building energy modeling plays a vital role in optimizing the operation of building energy systems by providing accurate predictions of the building's real-world conditions. In this context, various techniques have been explored, ranging from traditional p ...

2025

Coupled Electromagnetic-Electrophysiological Modeling of Neural Circuits

Joseph Tharayil

Much of our knowledge about the brain is derived from extracellular recordings of the electric fields produced by neural activity. However, interpretation of these signals is nontrivial. In par- ticular, it is difficult to link macro-scale observations of ...

EPFL2024

Distributed Optimization with Byzantine Robustness Guarantees

Lie He

As modern machine learning continues to achieve unprecedented benchmarks, the resource demands to train these advanced models grow drastically. This has led to a paradigm shift towards distributed training. However, the presence of adversariesâ whether ma ...

EPFL2023

Bayesian reconstruction of memories stored in neural networks from their connectivity

Lenka Zdeborová, Florent Gérard Krzakala

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in ...

2023

TaOx electron transport layers for CO2 reduction Si photocathodes

Raphaël François Lemerle, Elif Nur Dayi

Electron transport layers (ETLs) used as components of photocathodes for light-driven CO2 reduction (CO2R) in aqueous media should have good electronic transport, be stable under CO2R conditions, and, ideally, be catalytically inert for the competing hydro ...

Royal Society of Chemistry2023

Decoding bladder state from pudendal intraneural signals in pigs

Silvestro Micera, Stéphanie Lacour, Ivo Strauss, Valentina Marie Paggi, Leonardo Pollina

Neuroprosthetic devices used for the treatment of lower urinary tract dysfunction, such as incontinence or urinary retention, apply a pre-set continuous, open-loop stimulation paradigm, which can cause voiding dysfunctions due to neural adaptation. In the ...

2023

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

Alfio Quarteroni, Andrea Manzoni

Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as th ...

Wiley2023

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Rachid Guerraoui, Matteo Monti, Andrei Kucharavy

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

Assoc Computing Machinery2023

Robust Classification Using Contractive Hamiltonian Neural ODEs

Giancarlo Ferrari Trecate, Muhammad Zakwan, Liang Xu

Deep neural networks can be fragile and sensitive to small input perturbations that might cause a significant change in the output. In this letter, we employ contraction theory to improve the robustness of neural ODEs (NODEs). A dynamical system is contrac ...

2022

NeuralTree: A 256-Channel 0.227-mu J/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC

Stéphanie Lacour, Mahsa Shoaran, Bingzhao Zhu, Uisub Shin, Alix Esther Anne Trouillet, Cong Ding, Emilie Cornelia Maria Revol, Yashwanth Sai Reddy Vyza

Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Mismatched Estimation of Non-Symmetric Rank-One Matrices Under Gaussian Noise

Nicolas Macris, Farzad Pourkamali

We consider the estimation of a nxm matrix uv(T) observed through an additive Gaussian noise channel, a problem that frequently arises in statistics and machine learning. We investigate a scenario involving mismatched Bayesian inference in which the stat ...

IEEE2022

What Makes for Automatic Reconstruction of Pulmonary Segments

Li Zhang, Jiancheng Yang

3D reconstruction of pulmonary segments plays an important role in surgical treatment planning of lung cancer, which facilitates preservation of pulmonary function and helps ensure low recurrence rates. However, automatic reconstruction of pulmonary segmen ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval

Jie Luo, Mengshi Qi

With the current exponential growth of video-based social networks, video retrieval using natural language is receiving ever-increasing attention. Most existing approaches tackle this task by extracting individual frame-level spatial features to represent ...

2021

Une approche computationnelle du cadastre napoléonien de Venise

Federica Pardini, Frédéric Kaplan, Isabella Di Lenardo, Raphaël Barman

At the beginning of the 19th century, the Napoleonic administration introduced a new standardised description system to give an objective account of the form and functions of the city of Venice. The cadastre, deployed on a European scale, was offering for ...

2021

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Sabine Süsstrunk, Majed El Helou, Deblina Bhattacharjee

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are ...

IEEE Institute of Electrical and Electronics Engineers2021

Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

Kamiar Aminian, Mathieu Pascal Falbriard, Abolfazl Soltani

The overground speed is a key component of running analysis. Today, most speed estimation wearable systems are based on GNSS technology. However, these devices can suffer from sparse communication with the satellites and have a high-power consumption. In t ...

2021

Equation of State of Fluid Methane from First Principles with Machine Learning Potentials

Max David Veit

The predictive simulation of molecular liquids requires potential energy surface (PES) models that are not only accurate but also computationally efficient enough to handle the large systems and long time scales required for reliable prediction of macrosco ...

AMER CHEMICAL SOC2019