iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
Yes, but with a defined scope. A 2023 Nature study demonstrated a practical way to compare the free energy of pharmaceutical crystal forms while including temperature, relative humidity, hydrates and anhydrates. That makes computational screening more realistic than a fixed-temperature, dry-crystal ranking. The method can identify likely relative-stability changes and probable phase transitions, but its uncertainty is still too large to predict every transition temperature precisely or to tell researchers which polymorph will actually crystallize.
What the 2023 study changed
Crystal-structure prediction has traditionally been strongest at answering a thermodynamic question under idealized conditions: if several molecular arrangements exist, which one has the lowest calculated energy? Pharmaceutical development needs a harder answer. A solid form may encounter changing temperature and humidity during crystallization, drying, storage, shipping and use. Water can also become part of the lattice, producing hydrates with different stoichiometries from anhydrous forms.
| # | Preview | Product | Price | |
|---|---|---|---|---|
| 1 |
|
Drugs: From Discovery to Approval | $59.12 | Buy on Amazon |
| 2 |
|
Basic Principles of Drug Discovery and Development | $268.00 | Buy on Amazon |
| 3 |
|
Textbook of Drug Design and Discovery | $55.19 | Buy on Amazon |
| 4 |
|
Computational Drug Discovery and Design (Methods in Molecular Biology, 2714) | $139.46 | Buy on Amazon |
| 5 |
|
Drugs: From Discovery to Approval | $135.33 | Buy on Amazon |
Firaha and colleagues addressed that practical problem in “Predicting crystal form stability under real-world conditions,” published in Nature on 8 November 2023. Their TRHu(ST) 23 approach compares crystal-form free energies as temperature and relative humidity change, placing hydrate and anhydrate structures on a common energy landscape. The primary paper describes the method and its validation in detail: Nature, 2023.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →The advance is not a claim that software can replace laboratory solid-form work. It is a more realistic calculation for deciding which forms deserve attention, where a humidity-driven conversion is plausible, and how much confidence to attach to a predicted ranking.
#1 Best Overall
Why a drug’s crystal form matters
The same active molecule can crystallize in multiple polymorphs, hydrates or anhydrates. Those forms can differ in solubility, stability, melting behavior, compactability and bioavailability. Consequently, crystal form can affect tablet manufacture, formulation choices, shelf life and the amount of drug that reaches the patient.
A well-known warning is the ritonavir episode described by Chemistry World. After the medicine was introduced, a more stable but slower-nucleating polymorph appeared and disrupted supply. That historical event illustrates why discovering and controlling all relevant forms matters; the 2023 calculation was not used to predict or prevent it.
Humidity is not a minor laboratory detail. Water activity can change which solid is favored, particularly when a hydrate and anhydrate are close in free energy. Elsa Gheziel, co-chief executive of Avant-garde Materials Simulation, explained the geographic issue to Chemistry World: “If you have a lab in the UK the humidity will not be the same as in Hong Kong.” The statement is a practical explanation of why crystallization behavior can vary by location, not a quantified result from the Nature benchmark.
How TRHu(ST) 23 treats real conditions
Free energy rather than a single electronic energy
The method combines electronic-structure calculations with many-body dispersion corrections, finite-temperature vibrational free energies and additional molecular corrections. Vibrational terms matter because molecules and lattices do not remain still as temperature rises. Dispersion matters because weak intermolecular attractions can influence the relative stability of closely competing packings.
Humidity as a thermodynamic variable
For hydrate structures, the relevant comparison includes the chemical potential of water at a specified relative humidity. An anhydrate can therefore be favored in dry conditions while a hydrate becomes more stable as water activity increases. Instead of ranking every structure at one nominal temperature and zero humidity, TRHu(ST) 23 calculates how the ordering can move across a temperature–humidity range.
Hydrates and anhydrates on one landscape
Different hydrates may contain different numbers of water molecules per drug molecule. The approach puts those stoichiometries alongside anhydrates so that a possible conversion is treated as a free-energy crossing, rather than as an unrelated comparison between separate calculations.
Practical phonon calculations
Full phonon calculations can be expensive for molecular crystals. The study describes computational shortcuts that make the vibrational part tractable while retaining the finite-temperature contribution needed for a useful stability estimate. These are engineering choices for the study’s accuracy and throughput target, not a guarantee that every molecular crystal will have the same cost.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
What was actually validated
The Nature team assembled a benchmark from published measurements and experimental contributors in academia and industry. Complete paired structural and thermodynamic data are difficult to obtain, especially for metastable polymorphs, so the validation set should not be treated as a universal census of pharmaceutical crystals. The reported tests were:
| Validation item | Reported result | How to interpret it |
|---|---|---|
| Free-energy differences inferred from solubility ratios | 12 comparisons | A check against experimentally derived thermodynamic differences |
| Reversible anhydrate polymorph transitions | 4 transitions | Tests temperature-dependent ordering between anhydrous forms |
| Reversible hydrate–anhydrate transitions | 21 transitions | Tests humidity-sensitive phase behavior |
| Standard error of free-energy differences | About 1–2 kJ mol−1 for industrially relevant compounds | Study-specific uncertainty, not a universal guarantee |
| Hydrate–anhydrate transition relative humidity | Average reproduction within a factor of 1.7 | Average result for the study’s validation set |
| Energy error per water molecule | 0.641 kJ mol−1 standard deviation | Reported component of the error analysis |
| Energy error per non-water atom | 0.191 kJ mol−1 standard deviation | Reported component of the error analysis |
All figures in the table come from Firaha et al., Nature (2023), whose methods, compounds and definitions should be consulted before applying them to a new molecule: primary paper.
Why the uncertainty changes the meaning of a prediction
A 1–2 kJ mol−1 standard error is small enough to distinguish forms that are widely separated in free energy, but it can be decisive when two candidates are nearly tied. A calculated ranking should therefore be read with an uncertainty interval. If the predicted gap is smaller than the method’s error, the responsible conclusion is that the forms are thermodynamically unresolved at the current accuracy, not that the first-ranked form will necessarily dominate.
The authors state that predicting transition temperatures within 10 K would require an additional improvement in accuracy by a factor of 20. That estimate shows why a calculated phase boundary is best treated as a region of probable change. It is not a promise of a precise temperature at which a batch will transform.
The paper gives a particularly clear illustration for radiprodil: the predicted anhydrate–hydrate transition temperature carried a 183 K one-standard-deviation error. The authors describe that uncertainty as representative of observations in confidential contract research studies. It is an illustration of the present limitation, not a typical accuracy claim for every compound.
Rank #3
A practical workflow for a pharmaceutical team
1. Define the solid-form question
Specify whether the decision concerns an anhydrate polymorph, a particular hydrate stoichiometry, a drying condition, a storage excursion or a formulation property. Include the temperature and relative-humidity range that matters operationally. A calculation with no defined environment can answer only a narrower, abstract stability question.
2. Assemble defensible crystal structures
Supply experimentally solved structures where available and label whether each form is stable, metastable or merely hypothesized. Include the water content and molecular protonation state. Missing or incorrect structures cannot be rescued by a more sophisticated free-energy model.
3. Calculate and propagate uncertainty
Run the free-energy calculations for every relevant form across the chosen temperature–humidity grid. Report the predicted differences together with their standard errors. Flag crossings whose uncertainty bands overlap, because those are the cases in which a small model or structural error can change the ranking.
Recommended Free Tools
4. Use the map to prioritize experiments
Choose crystallization, slurry-conversion, drying and humidity-storage experiments that test the predicted boundaries. A form predicted to become more stable at high humidity deserves a controlled humidity challenge; a near-degenerate pair deserves broader screening rather than a premature single-form decision.
5. Confirm the material experimentally
Use the appropriate diffraction, thermal, spectroscopic and water-content measurements to verify identity and conversion. Computational stability does not establish particle morphology, nucleation rate, process robustness or bioavailability. Those properties still require laboratory and manufacturing evidence.
What the calculation can and cannot tell you
| Question | What the 2023 approach can support | What it does not establish |
|---|---|---|
| Which known form is more stable at a specified temperature and humidity? | A quantitative free-energy comparison with an uncertainty estimate | Certainty when the energy gap is within the reported error |
| Will a hydrate or anhydrate be favored? | Likely relative stability and a probable phase boundary | An exact transition point for every compound |
| Which polymorph will appear in a crystallizer? | Thermodynamic context for selecting experiments | Nucleation likelihood, growth rate or kinetic accessibility |
| Can an unobserved predicted structure be manufactured? | A way to rank its calculated stability if a valid structure is supplied | The solvent, supersaturation, seeding or process conditions needed to obtain it |
This distinction is central. Thermodynamic stability and crystallization likelihood are different variables. Chemistry World quotes Matteo Salvalaglio of University College London, who was not involved in the study, saying that “predicting the likelihood of observing polymorphs, and the conditions in which they can be obtained remains an open research question.” The same report describes Neumann’s interest in finding ways to crystallize structures predicted computationally but not yet observed experimentally: Chemistry World report.
One researcher quoted in that article characterized crystal-structure prediction as “more or less a solved problem.” That is an individual view about predicting possible structures, not a scientific consensus that the broader polymorph-and-process problem is solved. The practical gap between ranking a structure and reliably producing it remains consequential.
Computational cost and the software used
The study says its design target for a single calculation was about one day on 1,000 CPU cores. This is a description of the study’s computational planning, not a guaranteed hardware requirement for every user or molecule. Actual time depends on the number of candidate structures, supercell size, convergence settings and the temperature–humidity grid.
The free-energy calculations were originally implemented in and carried out with GRACE 3.0, a commercial package licensed from Avant-garde Materials Simulation. The paper says the extracted calculation library is available from AMS upon request. The cited sources do not establish current pricing, a trial, or a reader-facing purchase offer, so software availability should be confirmed directly with the vendor rather than inferred from the publication: Nature primary paper.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare this approach with other prediction strategies
For a technical team evaluating methods, the useful comparison is not a consumer-style product ranking. Ask whether a method includes the variables that control the decision and whether its validation matches the intended use.
- Environmental scope: Does it include finite-temperature vibrational effects and relative humidity, or only a zero-temperature electronic energy?
- Form coverage: Can it compare hydrates with different water stoichiometries and anhydrates on the same basis?
- Uncertainty: Are standard errors reported for free-energy differences, and are near-tied forms identified as unresolved?
- Validation: Are there reversible anhydrate and hydrate–anhydrate transitions tested against measured data?
- Throughput: What number of CPU cores and elapsed time are required for the candidate set and environmental grid?
- Decision scope: Does the output address thermodynamic stability only, or does it provide experimentally tested evidence about nucleation and crystallization likelihood?
Using these axes prevents a common category error: treating a method that ranks crystal energies as if it were also a process-development model.
Free tools Windows power users keep installed
One-click scans. No signup required.
What this means for development decisions
Early in a program, the method can help identify forms that should not be ignored simply because they are difficult to observe. It can also show that a form judged acceptable in a dry laboratory may be vulnerable to conversion during humid storage or transport. That information can guide humidity-controlled experiments, packaging specifications and the selection of a robust solid form.
Best Value
Later, the calculations can support a risk-based control strategy. A large free-energy margin across the intended environmental range is reassuring, while a crossing or near tie calls for tighter process controls and additional surveillance. Neither result replaces regulatory characterization: the calculation is evidence for prioritization and mechanistic understanding, not the sole basis for declaring a commercial form stable.
Teams should preserve the provenance of every input structure, water stoichiometry, temperature, humidity value and uncertainty estimate. Reproducibility is especially important when a predicted difference is only a fraction of a kilojoule per mole and when metastable forms are involved.
Remaining limitations and the next accuracy threshold
The benchmark is valuable but necessarily selective. Metastable polymorphs are hard to characterize completely, and paired structural and thermodynamic measurements are not available for every pharmaceutical compound. Results from the reported set therefore indicate capability, not a universal error bar that can be copied into a new project without qualification.
Model accuracy is only one bottleneck. Candidate generation, crystal defects, impurities, solvent history, agitation, seeding, supersaturation and drying kinetics can all determine what a laboratory actually isolates. A method can correctly identify the lower-free-energy form and still fail to predict a batch outcome if the competing form nucleates first or persists as a kinetically trapped solid.
The most useful near-term role is therefore integrated: calculate realistic free energies, expose uncertainty, design experiments around probable transitions, and feed measured outcomes back into process understanding. As accuracy improves, especially toward the factor-of-20 gain estimated for 10 K transition-temperature predictions, the calculations may support tighter operating windows. Until then, they are strongest as a quantitative map of relative stability under specified conditions.
Bottom line
Crystal-structure prediction has become more relevant to pharmaceutical work because TRHu(ST) 23 incorporates temperature, relative humidity, hydrates and anhydrates in one free-energy framework. In the 2023 Nature validation, it reproduced hydrate–anhydrate transition humidities within an average factor of 1.7 and reported roughly 1–2 kJ mol−1 standard errors for free-energy differences on industrially relevant compounds. Those results can focus solid-form screening and reveal environmental conversion risks. They do not yet predict every transition temperature precisely or determine which polymorph will crystallize. The winning strategy is to use the calculation as an uncertainty-aware guide for experiments and process control, not as a substitute for them.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute
