Formulation Development

Rules-Based Fragment Prediction for Mass Spectrometry

ACD/MS Fragmenter helps you predict mass spectral fragmentation in seconds. Have more confidence in your compound IDs, learn about fragmentation mechanisms, and publish your results easily.

It’s as simple as this:

• Draw your compound
• Select the ionization polarity and fragmentation options
• View its fragmentation tree

Reproducibility, reliability and carryover performance that meets your workflow demands, injection to injection, batch after batch.

ExionLC 2.0 and ExionLC 2.0+ systems provide full integration, control and compatibility with SCIEX mass spectrometers bringing together the speed and precision you expect for a complete solution that meets every challenge your lab faces.

The iCELLis™ bioreactor system is an automated, single-use, fixed-bed bioreactor that provides excellent cell growth conditions for adherent cells.

Standalone instrument with touchscreen and built-in sample ports for small volumes and cuvettes

The SpectraMax® QuickDrop™ Micro-Volume Spectrophotometer quantifies very small amounts of DNA, RNA, oligos, and proteins.

Upperton have developed UpperNose™, a nasal product development approach designed to streamline the development process for nasal dosage forms.

Rapid, high-throughput IgG quantification assay. Available in 96 & 384-well microplate options.

Calculators & Predictors for LogP, LogD, pK_a, Aqueous Solubility, and more…

The physicochemical properties of a molecule can help you better understand its likely behavior, support QSPR high-throughput screening (HTS) of libraries, and data-driven lead optimization.

ACD/PhysChem Suite is made up of a number of prediction modules. It provides high-quality, structure-based calculations of physicochemical properties.

• Predict aqueous solubility,^* boiling point, logD,^* logP,^* pK_a,^* Sigma, and other molecular descriptors for organic compounds, from structure
• Evaluate the calculated results with sorting, plotting tools
• Assess the reliability of predicted physicochemical property values
• Investigate quantitative structure-property relationships, structure modifications, and/or lead optimization for a target profile
• Train predictors with experimental data to better reflect novel chemical space
• Include custom models and in-house prediction algorithms

^*Models are trainable with experimental data

• Enables sample recovery
• Avoids dilution effects
• Analyzes samples in a matrix-free environment
• Has minimal buffer constraints