Glass Transition Temperature for Active Pharmaceutical Ingredients (API)

Tutorial Created with Software Release: 2026-1
Topics: Pharmaceutical Formulations
Methodology: All-Atom Molecular Dynamics
Products Used: Desmond, MS Maestro

Tutorial files

0.5 GB
This tutorial is written for use with a 3-button mouse with a scroll wheel.
Words found in the Glossary of Terms are shown like this: Workspacethe 3D display area in the center of the main window, where molecular structures are displayed

 

Tip: You can hover over a glossary term to display its definition. You can click on an image to expand it in the page.
Abstract:

 

In this tutorial, we will learn to predict thermophysical properties, namely glass transition temperature (Tg), of active pharmaceutical ingredients (APIs).

 

Tutorial Content

  1. Introduction

  1. Creating Projects and Importing Structures

  1. Building and Equilibrating Amorphous Cells

  1. Calculating Thermophysical Properties

  1. Conclusion and References

  1. Glossary of Terms

1. Introduction

Prediction of thermophysical and mechanical properties of drug compounds can provide key insights into formulation design. In particular, prediction of the glass transition temperature (Tg) for active pharmaceutical ingredients (APIs) is useful as i) an important indicator of thermodynamic stability in the solid state as well as for ii) predicting the overall material properties during their manufacturing process and storage conditions.

Materials Science Maestro enables a simple automated workflow for generating and fitting density-temperature curves for predicting Tg. Specifically, to find Tg, we run successive molecular dynamics (MD) simulations at variable temperatures, and analyze them with the Thermophysical Properties Calculation and Results panels. Tg is determined by fitting procedures based on the density transition inherently associated with a glassy to rubbery transition in amorphous solids.

In this tutorial, we will perform an automated, parallelized workflow for predicting Tg of three ubiquitous APIs: acemetacin, nandrolone and nizatidine. The workflow entails (1) preparing the small molecule inputs, (2) building a simulation box with the Disordered System Builder panel, (3) performing a MD Multistage Workflow to conformationally sample and then equilibrate the cells, (4) setting up and running the Thermophysical Properties workflow, and finally (5) viewing and analyzing the results.

The overall workflow is summarized in the following schematic:

For a related workflow for predicting Tg for a polymer system, see the Crosslinking Polymers and Polymer Property Prediction tutorials. For a related workflow for predicting solubility parameters for APIs, see the Molecular Dynamics Simulations for Active Pharmaceutical Ingredient (API) Miscibility tutorial.

2. Creating Projects and Importing Structures

At the start of the session, change the file path to your chosen Working Directorythe location that files are saved in MS Maestro to make file navigation easier. Each session in MS Maestro begins with a default Scratch Projecta temporary project in which work is not saved, closing a scratch project removes all current work and begins a new scratch project, which is not saved. A MS Maestro project stores all your data and has a .prj extension. A project may contain numerous entries corresponding to imported structures, as well as the output of modeling-related tasks. Once a project is saved, the project is automatically saved each time a change is made.

Structures can be built in MS Maestro or can be imported using File > Import Structures (or drag-and-dropped), and are added to the Entry Lista simplified view of the Project Table that allows you to perform basic operations such as selection and inclusion and Project Tabledisplays the contents of a project and is also an interface for performing operations on selected entries, viewing properties, and organizing structures and data. The Entry Lista simplified view of the Project Table that allows you to perform basic operations such as selection and inclusion is located to the left of the Workspacethe 3D display area in the center of the main window, where molecular structures are displayed. The Project Tabledisplays the contents of a project and is also an interface for performing operations on selected entries, viewing properties, and organizing structures and data can be accessed by Ctrl+T (Cmd+T) or Window > Project Table if you would like to see an expanded view of your project data.

  1. Double-click the Materials Science icon

Figure 2-1. Change Working Directory option.

  1. Go to File > Change Working Directory
  2. Find your directory, and click Choose
  3. Pre-generated files are included for running jobs or examining output. Download the zip file here: schrodinger.com/sites/default/files/s3/release/current/Tutorials/zip/api_tg.zip
  4. After downloading the zip file, unzip the contents in your working directorythe location that files are saved for ease of access throughout the tutorial

Figure 2-2. Save Project panel.

  1. Go to File > Save Project As
  2. Change the File name to api_tg_tutorial, click Save
    • The project is now named api_tg_tutorial.prj

Figure 2-3. Imported structures in the entry list (acemetacin is included in the workspace).

Three API structures are provided: acemetacin, nandrolone and nizatidine

  1. Go to File > Import Structures
  2. Navigate to where you downloaded the tutorial files (presumably in your working directory), and select input_APIs.mae. Click Open
    • A new entry group titled input_APIs (3) appears in the entry lista simplified view of the Project Table that allows you to perform basic operations such as selection and inclusion containing three entries

Note: The provided molecules were prepared in their neutral state, as they would be amorphized from their crystalline state or quenched into amorphous state i.e. without the intervention of any solvent molecules which may (de)protonate them. They were imported using the 2D Sketcher via their SMILES strings. SMILES specifications are used to represent molecules as strings and are commonly available for small molecules (see more). Subsequently, a force-field minimization was performed. For practice preparing organic molecules for simulations, see the Introduction to Maestro for Materials Science tutorial

3. Building and Equilibrating Amorphous Cells

In this section, we will use the Disordered System Builder and MD Multistage Workflow panels to build and equilibrate an amorphous cell for each API.

Figure 3-1. Selecting the entry group and opening the Disordered System Builder.

  1. Select(1) the atoms are chosen in the Workspace. These atoms are referred to as "the selection" or "the atom selection". Workspace operations are performed on the selected atoms. (2) The entry is chosen in the Entry List (and Project Table) and the row for the entry is highlighted. Project operations are performed on all selected entries the entire input_APIs (3) entry group from the entry lista simplified view of the Project Table that allows you to perform basic operations such as selection and inclusion
  2. Go to Tasks > Materials > Structure Builders > Disordered System
    • The Disordered System Builder panel opens
    • By default, all three entries are shown in the Components table. If you do not see all three entries, return to Step 1  

Note: For general practice with the Disordered System Builder, see the Disordered System Building and Molecular Dynamics Multistage Workflows tutorial

Figure 3-2. Setting the Components parameters.

We will generate three homogeneous cells, each containing 200 molecules.

  1. For Number of molecules, input 200
    • On the next tab, we will clarify that this should be number of molecules per cell (we can ignore the Molecules numbers in the table)
  2. Go to the Cells tab

 

 

Note: Each of the cells will contain 200 molecules, which is ~8,000-10,000 atoms. Though this box size is relatively small, a) we have kept the system manageable for the purposes of the tutorial and b) when the same workflow is performed with 500 molecules per box, the results are within ~5%. As always, there is a tradeoff between accuracy and computational expense.

Figure 3-3. Setting the Cells parameters.

  1. Uncheck All components
    • This avoids the construction of a mixed cell consisting of all three selected entries
  2. Check Homogeneous cell of each component
    • This instructs the builder to prepare homogeneous cells of the three APIs instead
  3. Maintain Full system
    • This ensures that each cell will have 200 molecules, as opposed to reading the values in the components table
  4. Maintain Number of cells of each type as 1
    • Changing this number would generate several replicates of each cell, which may be useful for better sampling (discussed further in Section 4)
  5. For Initial state, select Tangled chain

Note: Tangled chain rebuilds structures with rotatable bonds inside the box using a self-avoiding random walk algorithm (see the help documentation). This allows for relatively quicker builds and also facilitates some conformational space sampling. In some cases, another Initial state may be preferable (see discussion in Section 4)

  1. Change the Job name to disordered_system_APIs
  2. If you would like to run the job yourself, adjust your Job settings () as needed and click Run.
    • This job takes about 5-10 minutes on a CPU host

If you would prefer to proceed with pre-generated structures, you can import the amorphous cells via File > Import Structures. Navigate to where you downloaded the tutorial files and choose the Section_03 > disordered_system_APIs > disordered_system_APIs_componentN_system-out.cms (N=1,2,3)  files.

 

  1. Close the Disordered System Builder panel

Figure 3-4. The entry list after importing or incorporation (the acemetacin cell is shown in the workspace)

Feel free to visualize or stylize any of the output cells.

Before proceeding to the Tg calculation with the Thermophysical Properties panel, we will equilibrate our system.

In this case, the example molecules have many degrees of freedom and conformational orientations, and so it is recommended to perform some high temperature molecular dynamics before compressing the cell to allow for substantial conformational sampling.

Therefore, we will perform two molecular dynamics protocols on each cell:

Protocol 1: Materials Relaxation (a 3 stage robust protocol for quickly compressing the cell) followed by a Simulated Annealing from 300 K to 600 K (a heat ramp to allow for significant conformational sampling)

Protocol 2: Materials Relaxation (a 3 stage robust protocol for quickly compressing the cell after the heating) followed by NPT Molecular Dynamics (to equilibrate the cell)

We will split into the two protocols because it is good practice here to visualize the intermediate outputs for identification of eventual simulation artifacts (e.g. void zones), which would indicate that further cycles of equilibration are required.

Of course, different systems will require different equilibration. In this case, the resultant cells will be well-prepared for the Thermophysical Properties Calculations workflow.

For more practice on equilibrating cells with molecular dynamics, see the Disordered System Building and Molecular Dynamics Multistage Workflows tutorial. For another example of preparing a thermosetting polymer system for a thermophysical property calculation, see the Crosslinking Polymers and Polymer Property Prediction tutorials.

Figure 3-5. Selecting the entries and opening the MD Multistage Workflow panel.

We will first implement Protocol 1:

  1. Select(1) the atoms are chosen in the Workspace. These atoms are referred to as "the selection" or "the atom selection". Workspace operations are performed on the selected atoms. (2) The entry is chosen in the Entry List (and Project Table) and the row for the entry is highlighted. Project operations are performed on all selected entries all three new entries from the entry lista simplified view of the Project Table that allows you to perform basic operations such as selection and inclusion (Shift + Click on all 3 entries)
  2. Go to Tasks > Materials > Classical Mechanics > MD Simulations > MD Multistage Workflow

Figure 3-6. Setting up the MD Multistage Workflow panel. 

  1. Ensure that Use structures from directs to the 3 entries from above
  2. Check the box for Relaxation protocol and choose Materials Relaxation from the dropdown menu
  3. For Stage (4), change to Simulated Annealing
  4. Change the Number of stages to 2
  5. Set the Time to 0, 5000 and the Temperature to 300, 600
    • The system temperature will be linearly ramped from 300 to 600 K over the course of 5 ns
  6. Change the Simulation time to 5 ns and the Trajectory Recording interval to 500 ps
    • We will not be extracting any data from this simulation, so we will only record 10 frames from the trajectory
  7. Change the Ensemble class to NPT

Figure 3-7. Naming and running the job. 

  1. Change the Job name to multistage_simulation_hightemp
  2. Adjust the job settings () as needed and click Run
    • This job requires a GPU host. The job can be completed in about 2 hours on 3 GPUs.
  3. If you would prefer not to run the job, import the file via File > Import Structures:Section_03 > multistage_simulation_hightemp > multistage_simulation_hightemp__00N-out.cms (N=1,2,3)  from the provided files in the tutorial. Otherwise, click Run
  4. Close the MD Multistage Workflow panel

 

 

Note: For general practice with the MD Multistage Workflow panel, see the Disordered System Building and Molecular Dynamics Multistage Workflows tutorial

MD Simulations have a number of files associated with the job, for a full description of each file type see the help documentation on Desmond Files

Figure 3-8. Output after MD (the acemetacin cell is shown in the workspace) 

When the job is finished or after importing, feel free to stylize and visualize the outputs.

 

In general, it is always good practice to visualize the intermediate outputs for identification of eventual simulation artifacts (e.g. void zones), which would indicate that further cycles of equilibration are required. In this case, we do not see any such indication.

Figure 3-9. Selecting the entries and opening the MD Multistage Workflow panel.

Now we will implement Protocol 2:

  1. Select(1) the atoms are chosen in the Workspace. These atoms are referred to as "the selection" or "the atom selection". Workspace operations are performed on the selected atoms. (2) The entry is chosen in the Entry List (and Project Table) and the row for the entry is highlighted. Project operations are performed on all selected entries all three new entries from the entry lista simplified view of the Project Table that allows you to perform basic operations such as selection and inclusion (Shift + Click on all 3 entries)
    • Be sure the selected entries are the outputs from the first MD Multistage job
  2. Go to Tasks > Materials > Classical Mechanics > MD Simulations > MD Multistage Workflow
  3. For Stage (4), change to Molecular Dynamics

Figure 3-10. Setting up the MD Multistage Workflow panel. 

  1. Set the Simulation time (ns) to 3
  2. Set the Trajectory Recording interval (ps) to 125
    • This will generate 24 frames in the trajectory. Again, we will not be extracting any data from this simulation, so we will only record 24 frames from the trajectory
  3. Click Append Stage
    • A 5th stage appears in the workflow
  4. Change the new stage (Stage 5) to Analysis
  5. Change the Job name to multistage_simulation_relax
  6. Adjust the job settings () as needed
    • This job requires a GPU host. The job can be completed in about 1 hour on a 3 GPU host
  7. If you would like to run the job yourself, click Run. Otherwise, import the pre-generated files from the provided tutorial files via File > Import Structures:Section_03 > multistage_simulation_relax > multistage_simulation_relax_00N-out.cms (N=1,2,3)Close the MD Multistage Workflow panel

Figure 3-11. Output after MD (the acemetacin cell is shown in the workspace) 

When the job is finished or after importing, feel free to stylize and visualize the outputs. These cells are sufficiently equilibrated to proceed to the thermophysical properties calculations.

 

Optional: If interested, the bulk properties of the system over the course of the trajectory can be viewed with the MS MD Trajectory Analysis panel (via Tasks > Materials > Classical Mechanics > Trajectory Analysis > MS MD Trajectory Analysis). Note, only 24 frames per trajectory were recorded.

4. Calculating Thermophysical Properties

In this section, we will use the Thermophysical Properties panel to calculate Tg for the APIs. The Thermophysical Properties workflow performs a series of constant pressure and temperature MD simulations at varying temperatures. At the end of each simulation the equilibrium density and volume are recorded. That output is then used to obtain Tg by fitting the density versus temperature data. For more information on the methods used to calculate and extract the thermophysical properties, please see the following publications: High-Throughput Molecular Dynamics Simulations and Validation of Thermophysical Properties of Polymers for Various Applications and Uncertainty quantification in molecular dynamics studies of the glass transition temperature.

Figure 4-1. Selecting the entry group and opening the Thermophysical Properties panel.

  1. Select(1) the atoms are chosen in the Workspace. These atoms are referred to as "the selection" or "the atom selection". Workspace operations are performed on the selected atoms. (2) The entry is chosen in the Entry List (and Project Table) and the row for the entry is highlighted. Project operations are performed on all selected entries the three outputs from the second MD Multistage Workflow
  2. Go to Tasks > Materials > Classical Mechanics > Thermophysical Properties > Thermophysical Property Calculations

Figure 4-2. Setting the Thermophysical Property parameters.

To compute Tg, a temperature range that spans both the glassy (low temperature) and rubbery (high temperature) regions of the system needs to be defined. We will select a temperature range from 100 to 600 K for these examples. 

 

  1. In the Compute density section, for Over a range of temperatures between, enter 100 K and 600 K in steps of 10 K
  2. Ensure Start each temperature with the converged system from the previous temperature is checked
    • We can run the calculation from “High to Low” or “Low to high” temperature, here we leave the default of High to low
    • The simulations start at 600 K and cools down to 100 K with each previously converged system
    • Barostat isotropy should be left at the default of isotropic for molecular glasses like these APIs
  3. Set Simulation time to 5 ns
    • At each temperature, the density convergence will be checked after the simulation time, 5 ns
  4. Set Maximum cycles to 1
    • If the density is not converged after the simulation time, more MD simulations of the same time length will be performed if the Maximum cycles parameter is set to a number greater than 1
  5. Ensure Convergence is kept at 5%

Figure 4-3. Naming and running the Thermophysical Property job.

  1. Change the Job name to thermophysical_prop_APIs
  2. Adjust the job settings () as needed
    • This job requires a GPU host. The job can be completed in about 24 hours on 3 GPUs
  3. If you would like to run the job yourself, click Run. Otherwise, go to File > Import Structures, navigate to the provided tutorial files and Open  Section_04 > thermophysical_prop_APIs > thermophysical_prop_APIs-00N > thermophysical_prop_APIs-00N-out.cms where N = 1-3
  4. Close the Thermophysical Properties panel

Figure 4-4. Viewing the output system (the acemetacin output is shown in the workspace).

  1. When the job is finished or after importing, select(1) the atoms are chosen in the Workspace. These atoms are referred to as "the selection" or "the atom selection". Workspace operations are performed on the selected atoms. (2) The entry is chosen in the Entry List (and Project Table) and the row for the entry is highlighted. Project operations are performed on all selected entries and includethe entry is represented in the Workspace, the circle in the In column is blue one of the new 100.0_thermophysical_prop_APIs-00N-out entries from the corresponding MD: disordered_system_APIs_componentN_system (1) entry group
    • The displayed cell is from the last simulation at 100 K

 

Feel free to stylize and visualize the output systems in the workspacethe 3D display area in the center of the main window, where molecular structures are displayed

Figure 4-5. Opening the Thermophysical Properties Results panel via the WAM.

To get the Tg value from the Thermophysical Properties calculation we use the Thermophysical Properties Results panel:

 

  1. Select(1) the atoms are chosen in the Workspace. These atoms are referred to as "the selection" or "the atom selection". Workspace operations are performed on the selected atoms. (2) The entry is chosen in the Entry List (and Project Table) and the row for the entry is highlighted. Project operations are performed on all selected entries the output corresponding to acemetacin
  2. Use the WAM (workflow action menu) button () to open the Thermophysical Properties Results panel

Figure 4-6. Viewing the Hyperbola fit in the Thermophysical Properties Results panel.

Two methods are available to obtain the thermophysical properties of the system: a Bilinear fit, in which a linear regression is performed on the low temperature (glassy) region and the high temperature (rubbery) region of the plot of density versus temperature; and a Hyperbola fit, in which a single hyperbolic curve is fitted to all points of the plot of density versus temperature. By default, the Hyperbola fit is selected.

 

For acemetacin, the Tg is ~387 K.

Figure 4-7. Viewing the Bilinear fit in the Thermophysical Properties Results panel.

Now, let’s see what a Bilinear fit would give for Tg:

  1. Select Bilinear fit
  2. Click and move the edges of the blue and green boxes to readjust the boundaries between the low temperature (green) and high temperature (blue) regions to shift to the most linear regions. In the Figure, the green and blue regions are approximately set to 100 K to 250 K and 425 K to 575 K, respectively.
    • The resulting Tg value should be ~382 K which, in this case, is quite similar to that given by the hyperbolic fit

 

Note: The coefficient of thermal expansion can also be determined from the slope of the fit in the glassy region. Here, CTE is reported as 192.09 x 10-6 K-1

 

Note: The Store CTE and Tg button allows you to save the properties when you are satisfied with the fit to the data. The property is saved to the includedthe entry is represented in the Workspace, the circle in the In column is blue structure and can be displayed in the Project Tabledisplays the contents of a project and is also an interface for performing operations on selected entries, viewing properties, and organizing structures and data

Figure 4-8. Viewing the Uncertainty plot for the acemetacin Hyperbola fit.

Uncertainty values for either fitting procedure can be displayed on the Uncertainty tab

  1. Go to the Uncertainty tab
  2. Click Create
    • The plot updates to show the distribution of Tg values and their probabilities, respectively

 

The Fitting method can be selected and adjustments can be made to Sample size and number of bins. Feel free to explore these capabilities.

 

  1. Close the Thermophysical Properties Results panel

Proceed to perform similar analyses on the other two APIs:

 

Hyperbola fit for nandrolone

 

 

 

Hyperbola fit for nizatidine

 

 

 

Experimental Tg values for these molecules are reported in the literature (see the References section) and are summarized in the final column of the following table.

We can see that our calculated Tg values are somewhat higher than the experimental values. This is a frequently observed feature and is likely related to the limitations to represent in each staging time the macroscopic relaxations observed in experimental timescales. In fact, this is also an experimental issue, with some reported Tg values being highly dependent on the rate of temperature decrease. Some corrections, such as the William-Landel-Ferry [New Insight into Time-Temperature Correlation for Polymer Relaxations Ranging from Secondary Relaxation to Terminal Flow: Application of a Universal and Developed WLF Equation] offer a pathway to bring simulated (or experimental) Tg values close to their expected values. However, such corrections are oftentimes system-specific and may have difficult transferability.

We recently proposed a correction (see equation below) based on Tg data obtained from 315 polymeric systems in the article High-Throughput Molecular Dynamics Simulations and Validation of Thermophysical Properties of Polymers for Various Applications. Despite the obvious structural differences between API molecules and polymeric materials, we used this correction here as a first estimate to derive the Tg values of our amorphous API systems.:

(corrected Tg = calculated Tg * 0.77 + 21.08)

 

Here we summarize the results directly from the calculation via the two fitting methods, as well as after applying the numerical correction to each method:

Table 1. Calculated, Corrected and Experimentally Reported Tg Values

API

Calculated Tg (hyperbola)

Calculated Tg (bilinear)*

Corrected Tg (hyperbola)

Corrected Tg (bilinear)

Experimentally reported Tg

Acemetacin

387.09 K

381.79 K

319.14 K

315.06 K

310.15 K

Nandrolone

379.24 K

387.01 K

313.09 K

319.07 K

310.15 K

Nizatidine

274.75 K

303.30 K

232.64 K

254.62 K

286.15

*depending on the defined boundaries, values may vary; all values determined using 100-250 K and 425-575 K

 

By applying the correction, we see a good match between simulation- and experimentally-obtained  Tg values, particularly for acemetacin and nandrolone.

 

 

It is important to note that the molecules studied in this tutorial belong to a class of drugs which do not crystallize upon both cooling from the undercooled melt state and subsequent heating when studied experimentally, for instance, with differential scanning calorimetry (DSC). See the References section for further information about this classification system. Indeed, the specific workflow demonstrated herein is mainly recommended for such molecules. Drug molecules which exhibit crystallization upon cooling and/or reheating can still be studied with the thermophysical workflow, but may require different building or equilibration strategies for reliable Tg prediction.

Indeed, significant care should be taken when analyzing and interpreting Tg predictions for API molecules. Crystallization tendencies and the consequences of such tendencies can lead to complicated scenarios in which computed results must be interpreted with care.

For exploring other classes of drug molecules, there are many parameters that can be altered in the workflow to identify the most effective procedure. Here we offer some initial suggestions:

  • Replicate Sampling: having a significant number of replicates is a fundamental approach for best capturing the configuration space (see the Polymer Property Prediction tutorial for a replicate-based approach with uncertainty calculations)
  • Conformational Sampling: in Section 3, we used the tangled chain initial state to place the molecules in the disordered cell. Alternatively, one could imagine sampling the conformational space of the small molecules prior to building the cell, and then using the Amorphous or Snapped to grid placement methods for a Boltzmann-type distribution of conformations (see the Kinetic Monte Carlo Charge Mobility tutorial for steps for performing a conformational search and then constructing a disordered system based on a Boltzmann distribution)
  • Crystalline Inputs: Using a crystal structure as input and then submitting it to various annealing procedures at high temperatures can also be beneficial. Note, in this case, replicate sampling will not be so useful (the crystals will bear the same initial state), but sampling various annealing pressurization cycles could be explored (see the Molecular Dynamics Simulations for Active Pharmaceutical Ingredient (API) Miscibility tutorial for steps for preparing a crystalline structure for MD)
  • Equilibration Protocol: Of course, the equilibration protocol performed in this tutorial works well for these molecules, but can be altered for other systems (e.g. additional heating/pressurizing cycles, longer equilibration, etc.)

5.     Conclusion and References

In this tutorial, we learned how to predict the glass transition temperature of active pharmaceutical ingredients.

For further learning:

For introductory content, focused on navigating the Schrödinger Materials Science interface, an Introduction to Materials Science Maestro tutorial is available. Please visit the materials science training website for access to 100+ tutorials. For scientific inquiries or technical troubleshooting, submit a ticket to our Technical Support Scientists at help@schrodinger.com.

For self-paced, asynchronous, online courses in Materials Science modeling, including access to Schrödinger software, please visit the Schrödinger Online Learning portal on our website.

For some related practice, proceed to explore other relevant tutorials:

For further reading:

6.     Glossary of Terms

Entry List - a simplified view of the Project Table that allows you to perform basic operations such as selection and inclusion

Included - the entry is represented in the Workspace, the circle in the In column is blue

Project Table - displays the contents of a project and is also an interface for performing operations on selected entries, viewing properties, and organizing structures and data

Recent actions - This is a list of your recent actions, which you can use to reopen a panel, displayed below the Browse row. (Right-click to delete.)

Scratch Project - a temporary project in which work is not saved, closing a scratch project removes all current work and begins a new scratch project

Selected - (1) the atoms are chosen in the Workspace. These atoms are referred to as "the selection" or "the atom selection". Workspace operations are performed on the selected atoms. (2) The entry is chosen in the Entry List (and Project Table) and the row for the entry is highlighted. Project operations are performed on all selected entries

Working Directory - the location that files are saved

Workspace - the 3D display area in the center of the main window, where molecular structures are displayed