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Octanols

Octanols, also known as octyl alcohols, are a class of organic compounds with the chemical formula CnH2n+1OH, where n=8.
These saturated alcohols have a wide range of applications in various industries, including as solvents, plasticizers, and intermediates in the synthesis of other chemicals.
Octanols exhibit unique physical and chemical properties, such as low solubility in water, high lipophilicity, and the ability to form hydrogen bonds.
Researchers in the field of octanol chemistry often utilize literature, preprints, and patents to optimize their experimental protocols and identify the best products for their studies.
PubCompare.ai, an AI-driven platform, can help streamline this process by enabling intelligent comparisons and enhancing the reproducibility of octanol research, thus saving time and resources.

Most cited protocols related to «Octanols»

1
Decoy Generation for Ligand Benchmarking
Cited 38 times
As in the original DUD,
we property-matched decoys to ligands using molecular weight, estimated
water–octanol partition coefficient (miLogP), rotatable bonds,
hydrogen bond acceptors, and hydrogen bond donors, plus we added net
charge. We generated all ligand protonation states in pH range 6–8
using Schrödinger’s Epik with arguments “-ph
7.0 -pht 1.0 -tp 0.20” (Supporting Information
Figure S1C). Molecular properties were then computed using
Molinspiration’s mib. Over all the protonated forms of a given
ligand, we kept only those with a unique set of the six physicochemical
properties. For each of these unique property sets, we aimed to generate
50 matched decoys. For example, a single input ligand predicted to
have two alternate charges would get 50 decoys property-matched to
each charge. To accomplish this, a pool of decoys was selected from
ZINC46 (link) using a dynamic protocol that adapted
to local chemical space by narrowing or widening windows in seven
steps around the six properties. The goal was to return 3000–9000
potential decoys that matched the decoy’s reference protonation
state (predicted most prevalent form at pH 7.05). In the final decoy
procedure, ECFP4 fingerprints were generated by Scitegic’s
Pipeline Pilot for ligands and potential decoys. The decoys were sorted
by their maximum Tc to any ligand, and
the most dissimilar 25% were retained through this dissimilarity filter.
We then remove duplicate decoys from the ligand set by sorting decoys
from least to most duplicated and assigned each decoy to the protonated
ligand which has the least number of decoys already assigned. This
ensures unique decoys were spread across the ligands as evenly as
possible. Finally, if available, 50 decoys were picked randomly from
this deduplicated list.
Mysinger M.M., Carchia M., Irwin J.J, & Shoichet B.K. (2012). Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking. Journal of Medicinal Chemistry, 55(14), 6582-6594.
Publication 2012
Donors Hydrogen Bonds Ligands Octanols
2
Computational Analysis of Compound Properties
Cited 38 times
Physico-chemical properties were calculated using the Pipeline Pilot Chemistry Collection (version 8.0.1.500) from Accelrys (San Diego, CA, USA). The properties calculated were Molecular Weight (MW), octanol-water partition coefficient (ALOGP) (using the atom-based method by Ghose and Crippen24 ), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), molecular polar surface area (PSA), number of rotatable bonds (ROTB) and the number of aromatic rings (AROM)25 (link), 26 (link). Finally, a substructure search was performed against each drug using a curated reference set of 94 functional moieties that are potentially mutagenic, reactive or have unfavourable pharmacokinetic properties27 (link). The number of matches for each compound was captured (ALERTS). We chose to omit the acid dissociation constant (pKa) as the available high-throughput computational approaches do not provide sufficient accuracy48 (link).
Bickerton G.R., Paolini G.V., Besnard J., Muresan S, & Hopkins A.L. (2012). Quantifying the chemical beauty of drugs. Nature chemistry, 4(2), 90-98.
Publication 2012
Acids chemical properties Donors Hydrogen Bonds Mutagens Octanols Pharmaceutical Preparations
3
Ensemble UG-RNN Models for Predicting Aqueous Solubility
Cited 17 times
As previously mentioned, both the encoding function MG and the output mapping function MO are implemented using a neural network. In both cases, we use a standard three-layer neural network architecture, with one hidden layer. All neurons use a sigmoid transfer function (tanh) and weights are randomly initialized. In order to reduce the residual generalization error,67 we use an ensemble of 20 models with a different number of hidden units and features (i.e. the outputs units of MG), as described in Table 1. The optimal value of the learning rate η is determined by varying it from 10−1 to 10−4 and keeping the value which gives the lowest RMSE (Root Mean Square Error) (see Metrics). To facilitate learning, we slightly modify the gradient descent procedure as in.50 ,51 Specifically, the gradient of the error with respect to a weight dw is used to modify the weight according to the simple gradient descent rule Δw = −ηdw only if |dw| ∈ [0.1,1]. Outside this range, to avoid exploding or vanishing gradients, the learning rule is clipped: Δw = −ηsign(dw) if |dw| > 1, or Δw = −η0.1sign(dw) if |dw| < 0.1. Each UG-RNN model is trained for 5000 epochs and the outputs of the best 10 networks, selected by their Root Mean Square Error (RMSE) on the validation set, are averaged as an ensemble to compute the prediction on the test set, during each fold of the 10-fold cross validation procedure.
Because of the importance given to the octanol-water partition coefficient in the aqueous solubility literature, we also assess the performances of both the UG-RNN model and the UG-RNN-CR model using two different inputs for the output network MO. In addition to the case described above where the input to MO is the vector Gstructure alone, we also consider the case where the input consists of Gstructure plus the logPoctanol (calculated using Marvin Beans68 ). In this way, we can partially assess how logPoctanol affects the generalization capability of the UG-RNN and UG-RNN-CR models and better understand the kind of information contained in the vector Gstructure.
Lusci A., Pollastri G, & Baldi P. (2013). Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. Journal of chemical information and modeling, 53(7), 1563-1575.
Publication 2013
Cloning Vectors EPOCH protocol Generalization, Psychological Neurons Octanols Plant Roots Sigmoid Colon
4
Drosophila Olfactory Conditioning Assay
Cited 14 times
Groups of approximately 20 females of 4–10 d post-eclosion were trained and tested at 25°C at 50% relative humidity in a dark chamber. The flow rate of input air from each of the four arms was maintained at 100 mL/min throughout the experiments by mass-flow controllers, and air was extracted from the central hole at 400 mL/min. Odors were delivered to the arena by switching the direction of airflow to the tubes containing diluted odors using solenoid valves. The odors were diluted in paraffin oil (Sigma–Aldrich): 3-octanol (OCT; 1:1000; Merck) and 4-methylcyclohexanol (MCH; 1:750; Sigma–Aldrich), Pentyl acetate (PA: 1:5000; Sigma–Aldrich) and ethyl lactate (EL: 1:5000; Sigma–Aldrich). Shock and sugar conditioning was performed as previously described by using tubes with sucrose absorbed Whatman 3 MM paper or copper grids (Figure 2—figure supplement 3) (Aso et al., 2012 (link); Liu et al 2012 (link)). For the experiments in Figure 4B, a sheet of copper grid was placed at the bottom of arena. For appetitive memory assays, flies were starved for 24–48 hr on 1% agar. Videography was performed at 30 frames per second and analyzed using Fiji (Schindelin et al., 2012 (link)). Statistical comparisons were performed (Prism; Graphpad Inc, La Jolla, CA 92037) using the Kruskal Wallis test followed by Dunn's post-test for multiple comparison, except those in Figure 1F, Figure 2C and Figure 2—figure supplement 4 which used Wilcoxon signed-rank test with Bonferroni correction to compare from zero.
Aso Y, & Rubin G.M. (2016). Dopaminergic neurons write and update memories with cell-type-specific rules. eLife, 5, e16135.
Publication 2016
4-methylcyclohexanol Agar amyl acetate Arm, Upper Biological Assay Carbohydrates Copper Dietary Supplements Diptera ethyl lactate Females Humidity Memory Octanols Odors paraffin oils prisma Reading Frames Shock Sucrose
5
Computational Molecular Property Analysis
Cited 14 times
We use Frowns (developed by Brian Kelley), a chemoinformatics toolkit () written in Python and C++ to parse/read SMILES (see explanations about the format at ) or SDF files (see format at Molecular Design Limited).
We have implemented an algorithm in Python that make use of Frowns features to compute properties known to be important for filtering databases and that utilizes Xtool (38 ) to compute log P-values.
Because salts and counterions are often present in compound collections we recommend users to first apply the desalt utility that removes most salts and counterions prior to FAF-Drugs calculations.
Then, our program computes the following molecular properties:

(i) Molecular weight (part of Lipinski's RO5)

(ii) Hydrogen bond donors and acceptors (part of Lipinski's RO5)

Defined as the number of hydrogen bond acceptors (sum of N + O) and hydrogen bond donors (sum of OH + NH).

(iii) Number of rigid bonds

(iv) Number of rings

(v) Size of the rings

(vi) Number of rotatable bond

Defined as any single non-ring bond, bounded to non-terminal heavy atom (29 (link)). The amide C-N bonds are not considered because of their high rotational energy barrier.

(vii) Number of carbon atoms, number of heteroatoms and ratio.

(viii) Number of atom with a net charge

(ix) Sum of formal charges

(x) The Topological Polar Surface Area (TPSA)

The method described in (30 (link)) has been implemented. Briefly, the molecular polar surface area (PSA) (i.e. surface belonging to polar atoms) is a descriptor that was shown to correlate well with passive molecular transport through membranes. The calculation of PSA, however, is rather time-consuming because of the necessity to generate a reasonable 3D molecular geometry and the calculation of the surface itself. A new approach for the calculation of the PSA was developed by Erlt et al. (30 (link)) based on the summation of tabulated surface contributions of polar fragments. This approach was called topological polar surface area, it provides results that are practically identical with the 3D PSA while the computation speed is 2–3 orders of magnitude faster.

(xi) Computation of XlogP (P = calculated octanol/water partition coefficient) (part of Lipinski's RO5)

We use the XScore package () to compute XlogP as described in (38 ). This method gives log P-values by summing the contributions of component atoms while making use of correction factors. About 90 atom types are used to classify carbon, nitrogen, oxygen, sulfur, phosphorus and halogen atoms, and 10 correction factors are used for some special substructures. The contributions of each atom type and correction factor were derived by multivariate regression analysis of about 1850 organic compounds with known experimental log P-values.
In FAF-Drugs, the format for the input files has, for the time being, to be SDF, SMILES or CANSMILES while the compounds have to be in Mol2 format for XlogP computations. We use OpenBabel for file format conversion prior to XlogP calculations. Few compounds are found to have ambiguous atom types and in this case the log P is not computed. (Please see definitions about log P at: )

(xii) Atom check

Molecules with some specific atoms can be filtered-out (for instance molecules containing H, C, N, O, F, S, P, Cl, Br, I atoms are kept when using default parameters).
Miteva M.A., Violas S., Montes M., Gomez D., Tuffery P, & Villoutreix B.O. (2006). FAF-Drugs: free ADME/tox filtering of compound collections. Nucleic Acids Research, 34(Web Server issue), W738-W744.
Publication 2006
Amides Carbon Donors Halogens Hydrogen Bonds Muscle Rigidity Nitrogen Octanols Organic Chemicals Oxygen Pharmaceutical Preparations Phosphorus Python Salts single bond Sulfur Tissue, Membrane

Most recents protocols related to «Octanols»

1
Metallic Nanofiber Composition
Not available on PMC !

Example 15

    • A composition comprising:
    • about 0.01% to 3.0% of a plurality of functionalized metallic nanofibers;
    • a first solvent comprising about 3.0% to 7% 1-butanol, ethanol, 1-pentanol, n-methylpyrrolidone, 1-hexanol, or acetic acid, or mixtures thereof;
    • a viscosity modifier, resin, or binder comprising about 1.4% to 3.75% PVP, polyvinyl alcohol, or a polyimide, or mixtures thereof;
    • a second solvent comprising about 0.001% to 2% of 1-octanol, acetic acid, diethylene glycol, dipropylene glycol, propylene glycol, potassium hydroxide or sodium hydroxide, or mixtures thereof; and
    • with the balance comprising a third solvent such as cyclohexanol, cyclohexanone, cyclopentanone, cyclopentanol, butyl lactone, or mixtures thereof.

US11866827B2. Metallic nanofiber ink, substantially transparent conductor, and fabrication method (2024-01-09). NthDegree Technologies Worldwide Inc. [US]. Inventors: Mark David Lowenthal [US], Mark Lewandowski [US], Jeffrey Baldridge [US], Lixin Zheng [US], David Michael Chesler [US].
Patent 2024
1-hexanol 1-methyl-2-pyrrolidinone Acetic Acid Butyl Alcohol Cyclohexanol cyclohexanone cyclopentanol cyclopentanone diethylene glycol Ethanol Glycols Lactones Metals n-pentanol Octanols Polyvinyl Alcohol potassium hydroxide Propylene Glycol Resins, Plant Sodium Hydroxide Solvents Viscosity
2
Quantitative Bioanalysis of Drugs of Abuse in Urine
Not available on PMC !

Example 3

Biological samples such as urine were directly analyzed using the SFME nanoESI. FIG. 2 panels A-C. Calibration curves for quantitation of methamphetamine (FIG. 2 panel A), nicotine (FIG. 2 panel B), and benzoylecgonine (FIG. 2 panel C) in synthetic urine samples. 10 synthetic urine containing the drugs and internal standards were used as samples for the measurement. 5 μL ethyl acetate (EA) was used as the extraction phase for extraction, purification and spray. Internal standards: methamphetamine-d8 at 0.8 ng/mL, nicotine-d32 at ng/mL, benzoylecgonine-d3 at 1 ng/mL. The single reaction monitoring (SRM) transitions used: methamphetamine m/z 150→91, methamphetamine-d8 m/z 158→93; nicotine 163→130, nicotine-d3 m/z 166→130; benzoylecgonine m/z 290→168, benzoylecgonine-d3 m/z 293→171. Partition coefficients: LogPmethamphetamine=2.07; LogPnicotine=1.17, LogPbezoylecgonine=−0.59.

The matrix effect due to high concentration salts were minimized. Good LODs were obtained for drugs of abuse, even for benzoyecgonine with relatively low partition coefficient for the extraction phase. The partition coefficient (LogP) is defined as: LogP=log([solute]octanol/[solute]water), which represents the differential solubility of an un-ionized compound in an organic phase such as octanol immiscible with the aqueous phase at equilibrium.

US11875983B2. Systems and methods for quantifying an analyte extracted from a sample (2024-01-16). Purdue Research Foundation [US]. Inventors: Zheng Ouyang [US], Yue Ren [IN], Ziqing Lin [US].
Patent 2024
benzoylecgonine Biopharmaceuticals ethyl acetate Illicit Drugs Methamphetamine Nicotine Octanols Pharmaceutical Preparations Salts Urine Viscosity
3
Single and Multi-Cell Droplet Production
Droplets were produced using our previously published tip-loading platform (46 (link)). In short, cell and cytokine solutions were drawn into a 200 μl pipet tip and loaded onto the inlets in the PDMS chip. The pipet tip unloading was performed by a neMESYS microfluidic pump (Cetoni). HFE oil containing 2.5% PicoSurf (SphereFluidics) was used as the continuous phase at a flowrate of 30 μl/min for single-cell droplets and 10 μl/min for multi-cell droplets, while cells and the cytokines were flowed at a speed of 5 μl/min for single-cell droplets and 1.6 μl/min for multi-cell droplets. Cell concentration at the droplet formation point was 2×106 cells/ml for single-cell droplets and 2.5×106 (~2 cells/droplet), 5×106 (~4 cells/droplet), 10×106 (~8 cells/droplet), and 15×106 cells/ml (~12 cells/droplet) for the multi-cell droplets. During production, 2 µl of droplet suspension was imaged for each condition using an EVOS™ microscope (ThermoFisher scientific). To verify consistent droplet size and cell distribution these images were checked by measuring droplet diameter using ImageJ software. Droplets were collected in an Eppendorf tube from the outlet, and 150 µl of culture medium was added on top of the emulsions to prevent evaporation of HFE oil. Incubation of droplets was performed for 24 h at 37°C and 5% CO2, after which droplets were de-emulsified by addition of 20% 1H,1H,2H,2H-perfluoro-1-octanol (PFO) in HFE-7500 at a 1:1 volume ratio. After de-emulsification the upper interface of medium containing cells could be collected and processed for further analysis.
Tiemeijer B.M., Heester S., Sturtewagen A.Y., Smits A.I, & Tel J. (2023). Single-cell analysis reveals TLR-induced macrophage heterogeneity and quorum sensing dictate population wide anti-inflammatory feedback in response to LPS. Frontiers in Immunology, 14, 1135223.
Publication 2023
Cells Cytokine DNA Chips Emulsions HFE-7500 Microscopy Octanols
4
Determination of Peptide Octanol-Buffer Partition
Determination of log P was performed according to the method described by (Liu et al., 2006 (link)). Peptides were dissolved in 0.05 M HEPES buffer in 0.1 M NaCl, pH 7.4, then an equal volume of n-octanol was added and mixtures were vortexed for 2 min. Samples were centrifuged at 4,000 rpm for 1 min. After separation, aqueous and octanol phases were used to quantify peptide content by RP-HPLC.
Octanol phase was lyophilized and reconstituted in 80% acetonitrile in water containing 0.1% TFA before RP-HPLC. All n-octanol/buffer distribution studies were performed in triplicate.
Piekielna-Ciesielska J., Malfacini D., Djeujo F.M., Marconato C., Wtorek K., Calo’ G, & Janecka A. (2023). Functional selectivity of EM-2 analogs at the mu-opioid receptor. Frontiers in Pharmacology, 14, 1133961.
Publication 2023
1-Octanol acetonitrile Buffers HEPES High-Performance Liquid Chromatographies Octanols Peptides Sodium Chloride
5
Octanol-Water Partition Coefficient of Naproxen Salts
The octanol–water
partition coefficients of naproxen, naproxen sodium, and naproxen-based
ILs were determined by the shake-flask method described by the OECD
(Organizations for Economic Cooperation and Development) guidelines
combined with UV–vis.
First, water (pH 7.4) and n-octanol had to saturate each other. Second, the standard
solutions of different known concentrations of the naproxen salts
(naproxen sodium and naproxen-based ILs) in water saturated with n-octanol and naproxen in n-octanol saturated
with water were prepared sequentially, and the maximum absorption
wavelength was measured at 230 nm using a Cary60 UV–vis spectrometer
(Agilent, USA). The calibration curves were obtained. Third, a solution
was prepared by completely dissolving the solute, naproxen salts (naproxen
sodium and naproxen-based IL), in a 100 mL volumetric flask containing
water saturated with n-octanol. Approximately 5 mL
of this solution was added to a vial, and the same volume of n-octanol saturated with water was added. With naproxen
as the solute, naproxen was completely dissolved in a 100 mL volumetric
flask containing n-octanol saturated with water.
Approximately 5 mL of this solution was added to a vial, and the same
volume of water saturated with n-octanol was added.
For naproxen salts or naproxen, the solutions were prepared in
quintuplicate. The vials were placed in an oscillator at a constant
temperature of 25 °C for different periods. Subsequently, the
samples were centrifuged for 15 min at 5000 rpm to ensure complete
phase separation. Both phases were sampled by careful use of syringes.
The syringe used to collect the water-rich phase was filled with air,
which was slowly expelled while the syringe passed through the octanol
phase. After the absorbance value became constant, the equilibrium
time was obtained.
With the above-mentioned process and the
equilibrium time, the
concentrations in the water phase or n-octanol phase
were determined using a UV spectrophotometer at the characteristic
wavelength of naproxen (230 nm). By combining the calibration curves
and the initial concentration of naproxen sodium, naproxen-based ILs,
and naproxen, the equilibrium concentration of naproxen sodium, naproxen-based
ILs, and naproxen in two phases was obtained. In the Kow experiments, the measurement is always repeated three
times.
Tang Y., Yang K., Zhao S., Chen Q., Qin L, & Qin B. (2023). Evaluation of Solubility, Physicochemical Properties, and Cytotoxicity of Naproxen-Based Ionic Liquids. ACS Omega, 8(9), 8332-8340.
Publication 2023
1-Octanol Naproxen Naproxen Sodium Octanols Salts Syringes Tremor

Top products related to «Octanols»

1
Octanol by Merck Group
Sourced in United States, Germany, China
Octanol is a colorless, water-insoluble organic compound with the chemical formula CH3(CH2)7OH. It is a long-chain aliphatic alcohol. Octanol is commonly used as a laboratory reagent and in various industrial applications.
2
3 octanol by Merck Group
Sourced in United States, Spain, Germany
3-octanol is a chemical compound with the molecular formula C8H18O. It is a colorless, oily liquid with a mild, pleasant odor. 3-octanol is a type of alcohol that can be used as a solvent, intermediate in chemical synthesis, and in the production of fragrances and flavorings.
3
Qikprop by Schrödinger
Sourced in United States, Canada
QikProp is a computational tool developed by Schrödinger that predicts important physicochemical properties of drug-like molecules. It uses a knowledge-based approach to provide rapid and reliable estimates of various molecular properties, including solubility, permeability, and metabolic stability, which are crucial factors in drug development.
4
1 octanol by Merck Group
Sourced in United States, Germany, United Kingdom, India, China, Spain, Sao Tome and Principe
1-octanol is a chemical compound that is a primary alcohol with the molecular formula C8H18O. It is a colorless liquid with a mild, somewhat pleasant odor. 1-octanol has a variety of industrial and laboratory applications, including use as a solvent, emulsifier, and chemical intermediate.
5
Qikprop module by Schrödinger
Sourced in United States
QikProp is a module designed to quickly predict physical, chemical, and biological properties of organic compounds. It provides a rapid, automated way to calculate molecular descriptors and pharmaceutically relevant properties.
6
Methanol by Merck Group
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Methanol is a clear, colorless, and flammable liquid that is widely used in various industrial and laboratory applications. It serves as a solvent, fuel, and chemical intermediate. Methanol has a simple chemical formula of CH3OH and a boiling point of 64.7°C. It is a versatile compound that is widely used in the production of other chemicals, as well as in the fuel industry.
7
Perfluoro 1 octanol by Merck Group
Sourced in Germany
Perfluoro-1-octanol is a fluorinated organic compound with the chemical formula C8F17OH. It is a colorless liquid at room temperature with a high boiling point and low surface tension. The product is used in various industrial and laboratory applications as a specialty chemical.
8
2 octanol by Merck Group
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2-octanol is a chemical compound with the formula CH3(CH2)6CH2OH. It is a straight-chain secondary alcohol with eight carbon atoms. 2-octanol is a colorless liquid with a mild odor.
9
Linalool by Merck Group
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Linalool is a naturally occurring terpene alcohol found in various plant species. It is a colorless to pale yellow liquid with a floral, citrus-like aroma. Linalool is commonly used as a fragrance ingredient in personal care products and as a flavoring agent in food and beverages. Its core function is as a chemical precursor and intermediate in the synthesis of other compounds.
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Benzaldehyde by Merck Group
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Benzaldehyde is a clear, colorless liquid with a characteristic almond-like odor. It is a widely used organic compound that serves as a precursor and intermediate in the synthesis of various chemicals and pharmaceuticals.

More about "Octanols"

Octyl alcohols, also known as octanols, are a class of saturated organic compounds with the chemical formula CnH2n+1OH, where n=8.
These versatile compounds have a wide range of applications in various industries, serving as solvents, plasticizers, and intermediates in the synthesis of other chemicals.
Octanols exhibit unique physical and chemical properties, such as low solubility in water, high lipophilicity (fat-solubility), and the ability to form hydrogen bonds.
These characteristics make them valuable in numerous applications, including as components in personal care products, coatings, and lubricants.
Researchers in the field of octanol chemistry often utilize literature, preprints, and patents to optimize their experimental protocols and identify the best products for their studies.
PubCompare.ai, an AI-driven platform, can help streamline this process by enabling intelligent comparisons and enhancing the reproducibility of octanol research, saving time and resources.
In addition to octanols, related compounds like 3-octanol, 1-octanol, and 2-octanol, as well as other alcohols such as methanol and perfluoro-1-octanol, are also of interest in various industries and research applications.
Compounds like linalool and benzaldehyde, which share some structural similarities with octanols, may also be relevant in certain contexts.
By leveraging the power of AI-driven optimization, researchers can optimize their octanol studies, identify the best protocols and products, and enhance the reproducibility of their findings, ultimately driving progress in this dynamic field of chemistry.