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Found 27 papers, 8 papers with code
Open-Source Protein Language Models for Function Prediction and Protein Design
Protein language models (PLMs) have shown promise in improving the understanding of protein sequences, contributing to advances in areas such as function prediction and protein engineering.
Open source Differentiable ODE Solving Infrastructure
Ordinary Differential Equations (ODEs) are widely used in physics, chemistry, and biology to model dynamic systems, including reaction kinetics, population dynamics, and biological processes.
A Modular Open Source Framework for Genomic Variant Calling
Variant calling is a fundamental task in genomic research, essential for detecting genetic variations such as single nucleotide polymorphisms (SNPs) and insertions or deletions (indels).
Open Source Infrastructure for Automatic Cell Segmentation
Automated cell segmentation is crucial for various biological and medical applications, facilitating tasks like cell counting, morphology analysis, and drug discovery.
Open-Source Molecular Processing Pipeline for Generating Molecules
Generative models for molecules have shown considerable promise for use in computational chemistry, but remain difficult to use for non-experts.
Self-supervised Pretraining for Partial Differential Equations
We hypothesize that the model is in effect learning a family of operators (for multiple parameters) mapping the initial condition to the solution of the PDE at any future time step t. We compare this approach with the Fourier Neural Operator (FNO), and demonstrate that it can generalize over the space of PDE parameters, despite having a higher prediction error for individual parameter values compared to the FNO.
Open-Source Fermionic Neural Networks with Ionic Charge Initialization
Finding accurate solutions to the electronic Schr\"odinger equation plays an important role in discovering important molecular and material energies and characteristics.
Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning
Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces.
Open Source Infrastructure for Differentiable Density Functional Theory
Learning exchange correlation functionals, used in quantum chemistry calculations, from data has become increasingly important in recent years, but training such a functional requires sophisticated software infrastructure.
ChemBERTa-2: Towards Chemical Foundation Models
Large pretrained models such as GPT-3 have had tremendous impact on modern natural language processing by leveraging self-supervised learning to learn salient representations that can be used to readily finetune on a wide variety of downstream tasks.
Ranked #2 on
Molecular Property Prediction
on Clearance
Score-Based Generative Models for Molecule Generation
Recent advances in generative models have made exploring design spaces easier for de novo molecule generation.
FastFlows: Flow-Based Models for Molecular Graph Generation
We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules.
Bringing Atomistic Deep Learning to Prime Time
Artificial intelligence has not yet revolutionized the design of materials and molecules.
Differentiable Physics: A Position Piece
Differentiable physics provides a new approach for modeling and understanding the physical systems by pairing the new technology of differentiable programming with classical numerical methods for physical simulation.
Identification and Development of Therapeutics for COVID-19
A number of potential therapeutics against SARS-CoV-2 and the resultant COVID-19 illness were rapidly identified, leading to a large number of clinical trials investigating a variety of possible therapeutic approaches being initiated early on in the pandemic.
AutoMat: Accelerated Computational Electrochemical systems Discovery
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation.
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
GNNs and chemical fingerprints are the predominant approaches to representing molecules for property prediction.
AMPL: A Data-Driven Modeling Pipeline for Drug Discovery
The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of machine learning and molecular featurization tools.
PotentialNet for Molecular Property Prediction
The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales.
Retrosynthetic reaction prediction using neural sequence-to-sequence models
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem.
Ranked #33 on
Single-step retrosynthesis
on USPTO-50k
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose.
MoleculeNet: A Benchmark for Molecular Machine Learning
However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods.
Ranked #8 on
Molecular Property Prediction
on ESOL
Low Data Drug Discovery with One-shot Learning
Recent advances in machine learning have made significant contributions to drug discovery.
Ranked #2 on
Molecular Property Prediction
on Tox21
Learning Protein Dynamics with Metastable Switching Systems
We apply our EM algorithm to learn accurate dynamics from large simulation datasets for the opioid peptide met-enkephalin and the proto-oncogene Src-kinase.
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources.
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models
We present an EM algorithm for learning and introduce a model selection criteria based on the physical notion of convergence in relaxation timescales.
The Extended Parameter Filter
Drawing on an analogy to the extended Kalman filter, we develop and analyze, both theoretically and experimentally, a Taylor approximation to the parameter posterior that allows Storvik's method to be applied to a broader class of models.
