Uncertainty Quantification for In Silico Chemistry
Abstract
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational cost. The resulting abundance of data, generated by quantum chemistry, molecular dynamics sim- ulations, and chemical machine learning natu- rally raises questions about accuracy, precision, and reliability, as well as the systematic treat- ment of errors and uncertainties. Addressing these questions through rigorous mathematical frameworks is at the heart of Uncertainty Quan- tification. In the past years, the incorpora- tion of uncertainty quantification into in silico chemistry has gained attraction, motivated by its ability to provide deeper insights into chem- ical phenomena. In this review, we establish a common language for uncertainty quantifica- tion with respect to in silico chemistry, intro- duce the key mathematical formalisms, and sur- vey the growing body of work that applies un- certainty quantification across different areas of in silico chemistry.
How strong is the evidence?
Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- In silico toxicology protocols
- Automated in Silico Design of Homogeneous Catalysts
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.