The field of scientific research is rapidly advancing with the integration of artificial intelligence (AI) and machine learning (ML) techniques. Recent developments have focused on improving the efficiency and accuracy of scientific workflows, particularly in the areas of bioinformatics, chemistry, and materials science. The use of large language models (LLMs) has shown significant promise in automating tasks such as data analysis, hypothesis generation, and experiment design. Additionally, the application of deep learning techniques has enabled the development of more accurate models for predicting molecular properties, protein structures, and genomic sequences. Noteworthy papers in this area include Innovator, which introduces a novel approach to continued pretraining of LLMs for scientific tasks, and TrinityDNA, which proposes a bio-inspired foundational model for efficient long-sequence DNA modeling. Other notable works include the development of multimodal infinite polymer sequence pre-training frameworks, zero-shot learning approaches for compound-protein interaction prediction, and hyperbolic genome embeddings for more expressive DNA sequence representations. Overall, these advances have the potential to revolutionize the field of scientific research by enabling faster, more accurate, and more efficient discovery of new knowledge.
Advances in AI-Driven Scientific Research
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Innovator: Scientific Continued Pretraining with Fine-grained MoE Upcycling
Exploring molecular assembly as a biosignature using mass spectrometry and machine learning
TrinityDNA: A Bio-Inspired Foundational Model for Efficient Long-Sequence DNA Modeling
Language Models for Controllable DNA Sequence Design
From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics
A Multi-Agent System for Information Extraction from the Chemical Literature
SciToolAgent: A Knowledge Graph-Driven Scientific Agent for Multi-Tool Integration
MIPS: a Multimodal Infinite Polymer Sequence Pre-training Framework for Polymer Property Prediction
ResCap-DBP: A Lightweight Residual-Capsule Network for Accurate DNA-Binding Protein Prediction Using Global ProteinBERT Embeddings
Deep Generative Models of Evolution: SNP-level Population Adaptation by Genomic Linkage Incorporation
Zero-Shot Learning with Subsequence Reordering Pretraining for Compound-Protein Interaction
GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis
An LLM Driven Agent Framework for Automated Infrared Spectral Multi Task Reasoning
Hyperbolic Genome Embeddings
TempRe: Template generation for single and direct multi-step retrosynthesis
ChemDFM-R: An Chemical Reasoner LLM Enhanced with Atomized Chemical Knowledge
SmilesT5: Domain-specific pretraining for molecular language models
EB-gMCR: Energy-Based Generative Modeling for Signal Unmixing and Multivariate Curve Resolution