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Found 27 papers, 17 papers with code
Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research
First, we test two different families of acquisition functions in two synthetic problems and study the effect of the informativeness and cost of the approximate function.
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants
We investigate the potential scale of this vulnerability by measuring the degree to which AI assistants can complete assessment questions in standard university-level STEM courses.
Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models
Synthesizability in generative molecular design remains a pressing challenge.
Gradient Guided Hypotheses: A unified solution to enable machine learning models on scarce and noisy data regimes
This framework entails an additional step in machine learning training, where gradients can be included or excluded from backpropagation.
Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation
Saturn outperforms 22 models on multi-parameter optimization tasks relevant to drug discovery and may possess sufficient sample efficiency to consider the prospect of directly optimizing high-fidelity oracles.
Are large language models superhuman chemists?
Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained.
Molecular Hypergraph Neural Networks
To tackle this challenge, we introduce molecular hypergraphs and propose Molecular Hypergraph Neural Networks (MHNN) to predict the optoelectronic properties of organic semiconductors, where hyperedges represent conjugated structures.
FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise
The FSscore showcases how human expert feedback can be utilized to optimize the assessment of synthetic feasibility for a variety of applications.
Holistic chemical evaluation reveals pitfalls in reaction prediction models
The prediction of chemical reactions has gained significant interest within the machine learning community in recent years, owing to its complexity and crucial applications in chemistry.
Extracting human interpretable structure-property relationships in chemistry using XAI and large language models
Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
We introduce ODEFormer, the first transformer able to infer multidimensional ordinary differential equation (ODE) systems in symbolic form from the observation of a single solution trajectory.
Transformers and Large Language Models for Chemistry and Drug Discovery
Language modeling has seen impressive progress over the last years, mainly prompted by the invention of the Transformer architecture, sparking a revolution in many fields of machine learning, with breakthroughs in chemistry and biology.
Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design
Generative molecular design has moved from proof-of-concept to real-world applicability, as marked by the surge in very recent papers reporting experimental validation.
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Recent studies suggested that these models could be useful in chemistry and materials science.
Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design
Molecular generative models have shown remarkable sample efficiency when coupled with reinforcement learning, as demonstrated in the Practical Molecular Optimization (PMO) benchmark.
ChemCrow: Augmenting large-language models with chemistry tools
Our agent autonomously planned and executed the syntheses of an insect repellent, three organocatalysts, and guided the discovery of a novel chromophore.
GAUCHE: A Library for Gaussian Processes in Chemistry
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
SELFIES and the future of molecular string representations
We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
Identification of Enzymatic Active Sites with Unsupervised Language Modeling
Herein, we apply a Transformer architecture to a language representation of bio-catalyzed chemical reactions to learn the signal at the base of the substrate-active site atomic interactions.
Human-in-the-loop for a Disconnection Aware Retrosynthesis
In this work, we design an approach to provide this option by adapting a transformer-based model for single-step retrosynthesis.
Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design
Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning.
Unassisted Noise Reduction of Chemical Reaction Data Sets
Existing deep learning models applied to reaction prediction in organic chemistry can reach high levels of accuracy (> 90% for Natural Language Processing-based ones).
Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks
Organic reactions are usually assigned to classes containing reactions with similar reagents and mechanisms.
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge.
Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy
We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention.
Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction
Organic synthesis is one of the key stumbling blocks in medicinal chemistry.
"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models
With this approach, we demonstrate results superior to the state-of-the-art solution by a significant margin on the top-1 accuracy.
