Castrate-resistant prostate cancer response to taxane is determined by an HNF1-dependent apoptosis resistance circuit.

Metastatic castrate-resistant prostate cancer (mCRPC) is a genetically and phenotypically heterogeneous cancer where advancements are needed in biomarker discovery and targeted therapy. A critical and often effective component of treatment includes taxanes. We perform a high-throughput screen across a cohort of 30 diverse patient-derived castrate-resistant prostate cancer (CRPC) organoids to a library of 78 drugs.

Dynamics Calculations of the Flexibility and Vibrational Spectrum of the Linear Alkane CH, Based on Machine-Learned Potentials.

Hydrocarbons are the central feedstock of fuels, solvents, lubricants, and the starting materials for many synthetic materials, and thus the physical properties of hydrocarbons have received intense study. Among these, the molecular flexibility and the power and infrared spectroscopies are the focus of this paper. These are examined for the linear alkane CH using molecular dynamics (MD) calculations and recent machine-learned potentials.

On the nature of hydrogen bonding in the HS dimer.

Hydrogen bonding is a central concept in chemistry and biochemistry, and so it continues to attract intense study. Here, we examine hydrogen bonding in the HS dimer, in comparison with the well-studied water dimer, in unprecedented detail. We record a mass-selected IR spectrum of the HS dimer in superfluid helium nanodroplets.

DFT-Based Permutationally Invariant Polynomial Potentials Capture the Twists and Turns of CH.

Hydrocarbons are ubiquitous as fuels, solvents, lubricants, and as the principal components of plastics and fibers, yet our ability to predict their dynamical properties is limited to force-field mechanics. Here, we report two machine-learned potential energy surfaces (PESs) for the linear 44-atom hydrocarbon CH using an extensive data set of roughly 250,000 density functional theory (DFT) (B3LYP) energies for a large variety of configurations, obtained using MM3 direct-dynamics calculations at 500, 1000, and 2500 K. The surfaces, based on Permutationally Invariant Polynomials (PIPs) and using both a many-body expansion approach and a fragmented-basis approach, produce precise fits for energies and forces and also produce excellent out-of-sample agreement with direct DFT calculations for torsional and dihedral angle potentials.

Calculations of Dissociation Dynamics of CHOH on a Global Potential Energy Surface Reveal the Mechanism for the Formation of HCOH; Roaming Plays a Role.

The experimental observation of hydroxymethylene, HCOH, following excitation of methanol at 193 nm, was reported recently (Hockey, E. K.; McLane, N.

Fully Quantum Simulation of Polaritonic Vibrational Spectra of Large Cavity-Molecule System.

The formation of molecular vibrational polaritons, arising from the interplay between molecular vibrations and infrared cavity modes, is a quantum phenomenon necessitating accurate quantum dynamical simulations. Here, we introduce the cavity vibrational self-consistent field/virtual state configuration interaction method, enabling quantum simulation of the vibrational spectra of many-molecule systems within the optical cavity. Focusing on the representative (HO) system, we showcase this parameter-free quantum approach's ability to capture both linear and nonlinear vibrational spectral features.

Reproducible preclinical models of androgen receptor driven human prostate cancer bone metastasis.

Background: Preclinical models recapitulating the metastatic phenotypes are essential for developing the next-generation therapies for metastatic prostate cancer (mPC). We aimed to establish a cohort of clinically relevant mPC models, particularly androgen receptor positive (AR) bone metastasis models, from LuCaP patient-derived xenografts (PDX) that reflect the heterogeneity and complexity of mPC.

Methods: PDX tumors were dissociated into single cells, modified to express luciferase, and were inoculated into NSG mice via intracardiac injection.

Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine.

Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PESs) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from the same distribution as the training data. Here, we systematically investigate the relationship between such test errors and the simulation accuracy with MLPs on an example of a full-dimensional, global PES for the glycine amino acid.

Formic Acid-Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer.

The formic acid-ammonia dimer is an important example of a hydrogen-bonded complex in which a double proton transfer can occur. Its microwave spectrum has recently been reported and rotational constants and quadrupole coupling constants were determined. Calculated estimates of the double-well barrier and the internal barriers to rotation were also reported.

Ab Initio Potential Energy Surface for NaCl-H with Correct Long-Range Behavior.

We report a full dimensional ab initio potential energy surface for NaCl-H based on precise fitting of a large data set of CCSD(T)/aug-cc-pVTZ energies. A major goal of this fit is to describe the very long-range interaction accurately. This is done in this instance via the dipole-quadrupole interaction.

A New Method to Avoid Calculation of Negligible Hamiltonian Matrix Elements in CI Calculation.

Hamiltonian matrices typically contain many elements that are negligibly small compared to the diagonal elements, even with methods to prune the underlying basis. Because for general potentials the calculation of -matrix elements is a major part of the computational effort to obtain eigenvalues and eigenfunctions of the Hamiltonian, there is strong motivation to investigate locating these negligible elements without calculating them or at least avoid calculating them. We recently demonstrated an effective means to "learn" negligible elements using machine learning classification ( , 159, 071101).

Tumor-derived biomarkers predict efficacy of B7H3 antibody-drug conjugate treatment in metastatic prostate cancer models.

Antibody-drug conjugates (ADCs) are a promising targeted cancer therapy; however, patient selection based solely on target antigen expression without consideration for cytotoxic payload vulnerabilities has plateaued clinical benefits. Biomarkers to capture patients who might benefit from specific ADCs have not been systematically determined for any cancer. We present a comprehensive therapeutic and biomarker analysis of a B7H3-ADC with pyrrolobenzodiazepine(PBD) payload in 26 treatment-resistant, metastatic prostate cancer (mPC) models.

A Status Report on "Gold Standard" Machine-Learned Potentials for Water.

Owing to the central importance of water to life as well as its unusual properties, potentials for water have been the subject of extensive research over the past 50 years. Recently, five potentials based on different machine learning approaches have been reported that are at or near the "gold standard" CCSD(T) level of theory. The development of such high-level potentials enables efficient and accurate simulations of water systems using classical and quantum dynamical approaches.

Machine learning classification can significantly reduce the cost of calculating the Hamiltonian matrix in CI calculations.

Hamiltonian matrices in electronic and nuclear contexts are highly computation intensive to calculate, mainly due to the cost for the potential matrix. Typically, these matrices contain many off-diagonal elements that are orders of magnitude smaller than diagonal elements. We illustrate that here for vibrational H-matrices of H2O, C2H3 (vinyl), and C2H5NO2 (glycine) using full-dimensional ab initio-based potential surfaces.

Manipulating hydrogen bond dissociation rates and mechanisms in water dimer through vibrational strong coupling.

The vibrational strong coupling (VSC) between molecular vibrations and cavity photon modes has recently emerged as a promising tool for influencing chemical reactivities. Despite numerous experimental and theoretical efforts, the underlying mechanism of VSC effects remains elusive. In this study, we combine state-of-art quantum cavity vibrational self-consistent field/configuration interaction theory (cav-VSCF/VCI), quasi-classical trajectory method, along with the quantum-chemical CCSD(T)-level machine learning potential, to simulate the hydrogen bond dissociation dynamics of water dimer under VSC.

Interfacing q-AQUA with a Polarizable Force Field: The Best of Both Worlds.

Polarizable force fields are pervasive in the fields of computational chemistry and biochemistry; however, their empirical or semiempirical nature gives them both weaknesses and strengths. Here, we have developed a hybrid water potential, named q-AQUA-pol, by combining our recent q-AQUA potential with the TTM3-F water potential. The new potential demonstrates unprecedented accuracy ranging from gas-phase clusters, e.

Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands.

Tropolone, a 15-atom cyclic molecule, has received much interest both experimentally and theoretically due to its H-transfer tunneling dynamics. An accurate theoretical description is challenging owing to the need to develop a high-level potential energy surface (PES) and then to simulate quantum-mechanical tunneling on this PES in full dimensionality. Here, we tackle both aspects of this challenge and make detailed comparisons with experiments for numerous isotopomers.

PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials.

We wish to describe a potential energy surface by using a basis of permutationally invariant polynomials whose coefficients will be determined by numerical regression so as to smoothly fit a dataset of electronic energies as well as, perhaps, gradients. The polynomials will be powers of transformed internuclear distances, usually either Morse variables, exp(-r/λ), where λ is a constant range hyperparameter, or reciprocals of the distances, 1/r. The question we address is how to create the most efficient basis, including (a) which polynomials to keep or discard, (b) how many polynomials will be needed, (c) how to make sure the polynomials correctly reproduce the zero interaction at a large distance, (d) how to ensure special symmetries, and (e) how to calculate gradients efficiently.

Δ-Machine Learned Potential Energy Surfaces and Force Fields.

There has been great progress in developing machine-learned potential energy surfaces (PESs) for molecules and clusters with more than 10 atoms. Unfortunately, this number of atoms generally limits the level of electronic structure theory to less than the "gold standard" CCSD(T) level. Indeed, for the well-known MD17 dataset for molecules with 9-20 atoms, all of the energies and forces were obtained with DFT calculations (PBE).

Semiclassical and VSCF/VCI Calculations of the Vibrational Energies of - and -Ethanol Using a CCSD(T) Potential Energy Surface.

A recent full-dimensional Δ-Machine learning potential energy surface (PES) for ethanol is employed in semiclassical and vibrational self-consistent field (VSCF) and virtual-state configuration interaction (VCI) calculations, using MULTIMODE, to determine the anharmonic vibrational frequencies of vibration for both the and conformers of ethanol. Both semiclassical and VSCF/VCI energies agree well with the experimental data. We find significant mixing between the VSCF basis states due to Fermi resonances between bending and stretching modes.

Nonadiabatic Reactive Quenching of OH(Σ) by H: Origin of High Vibrational Excitation in the HO Product.

The nonadiabatic dynamics of the reactive quenching channel of the OH(Σ) + H/D collisions is investigated with a semiclassical surface hopping method, using a recently developed four-state diabatic potential energy matrix (DPEM). In agreement with experimental observations, the HO/HOD products are found to have significant vibrational excitation. Using a Gaussian binning method, the HO vibrational state distribution is determined.