Nature Computational Science

@NatComputSci

A @SpringerNature journal on mathematical models and computational methods/tools that help advance science in multiple disciplines.

New York, NY

Joined April 2020

329Following

7KFollowers

Pinned

Nature Computational Science@NatComputSci · 12 h

🚨Our July issue is now live and includes research on adverse drug reactions, multi-fidelity Bayesian optimization, rare event sampling, and much more! nature.com/natcomputsci/v…

NatComputSci's tweet image. 🚨Our July issue is now live and includes research on adverse drug reactions, multi-fidelity Bayesian optimization, rare event sampling, and much more!
nature.com/natcomputsci/v…

1.0K

Nature Computational Science@NatComputSci · 12 h

📢In this month's editorial, we provide our recommendations for effectively writing computational science manuscripts. nature.com/articles/s4358…

710

Nature Computational Science@NatComputSci · Jul 11

Researchers from @cosybio_UHH present FedProt which enables privacy-preserving, federated differential protein abundance analysis across multiple institutions. nature.com/articles/s4358…

NatComputSci's tweet card. Nature Computational Science - In this Resource, the authors present FedProt, a tool that enables privacy-preserving, federated differential protein abundance analysis across multiple institutions....

768

Nature Computational Science@NatComputSci · Jul 8

Out now! @AlissaHummer, @OPIGlets and colleagues present Graphinity, a method to predict change in antibody-antigen binding affinity (∆∆G). Featuring synthetic datasets of ~1 million FoldX-generated and >20,000 Rosetta Flex ddG-generated ∆∆G values! nature.com/articles/s4358…

NatComputSci's tweet card. Nature Computational Science - Predicting the effects of mutations on antibody–antigen binding is a key challenge in therapeutic antibody development. Orders of magnitude more data will be...

5.0K

Nature Computational Science@NatComputSci · Jul 7

Out now! Xiaoqiong Li and colleagues develop an approach, OrganADR, to predict adverse drug reactions at the organ level for emerging combination pharmacotherapy nature.com/articles/s4358… 🔓rdcu.be/evcgl

NatComputSci's tweet card. Nature Computational Science - An associative-learning-enhanced model, called OrganADR, is developed to predict adverse drug reactions at the organ level. OrganADR could be applied in emerging...

424

Nature Computational Science@NatComputSci · Jun 27

Out now! Shuilin Jin and colleagues report a metric to quantify gene-level batch effects in single-cell data. nature.com/articles/s4358… 🔓rdcu.be/etNNO

NatComputSci's tweet card. Nature Computational Science - Group technical effects, a metric to quantify gene-level batch effects in single-cell data, is proposed. The analysis based on group technical effects reveals the...

808

Nature Computational Science@NatComputSci · Jun 27

Out now! @hellen42085170 and colleagues present RNAsmol, a sequence-based framework for predicting RNA-small molecule interactions. #DrugDiscovery nature.com/articles/s4358… 🔓rdcu.be/etNJk

NatComputSci's tweet card. Nature Computational Science - RNAsmol is a sequence-based deep learning framework for predicting RNA–small molecule interactions, integrating perturbation and augmentation to achieve robust...

547

Nature Computational Science@NatComputSci · Jun 26

🚨Our June issue is now live and includes research on cell-cell communication, a study on gender and racial diversity in science, and much more! nature.com/natcomputsci/v…

NatComputSci's tweet image. 🚨Our June issue is now live and includes research on cell-cell communication, a study on gender and racial diversity in science, and much more!
nature.com/natcomputsci/v…

2.0K

Nature Computational Science@NatComputSci · Jun 24

A Perspective from @gabepgomes and colleagues in @gpggrp covers the opportunities for LLMs to advance chemical research and the challenges that must be overcome to effectively use LLMs as scientific partners. @CMU_Chem @CMU_ChemE nature.com/articles/s4358… 🔓rdcu.be/etcqs

NatComputSci's tweet card. Nature Computational Science - This Perspective highlights the potential integrations of large language models (LLMs) in chemical research and provides guidance on the effective use of LLMs as...

2.0K

Nature Computational Science@NatComputSci · Jun 19

📢Shaoting Zhang and colleagues propose CLOVER, a cost-effective instruction learning framework for conversational pathology. nature.com/articles/s4358… 🔓rdcu.be/erQld

NatComputSci's tweet card. Nature Computational Science - Training foundation models often requires a costly budget and excessive computational resources. In this study, a low-cost instruction learning framework is proposed...

544

Nature Computational Science@NatComputSci · Jun 18

📢Out now! Haohao Fu and colleagues introduce a high-throughput, formally exact method for absolute binding free energy calculations. nature.com/articles/s4358… 🔓rdcu.be/erBRm

NatComputSci's tweet card. Nature Computational Science - A thermodynamic cycle is proposed that limits protein–ligand relative motion and boosts the efficiency of absolute binding-free-energy calculations four- to...

530

Nature Computational Science@NatComputSci · Jun 10

📢@XuhuiHuangChem and colleagues present MEMnets, combining statistical mechanics theory with DL to find the slowest collective variables for biomolecular dynamics. @UWMadisonChem, @datascience_uw, @TCI_UW_Madison, @UWMadisonLS nature.com/articles/s4358… 🔓rdcu.be/eqnCf

NatComputSci's tweet card. Nature Computational Science - This study presents MEMnets, an approach that integrates statistical mechanics with deep learning to identify the slowest collective variables for biomolecular...

4.0K