quantum mechanics Archives - Pharmacelera | Pushing the limits of computational chemistry

A new standardized protocol for the preparation of large 3D fully enumerated compound libraries

Enric Herrero — Thu, 07 Nov 2024 10:39:40 +0000

By Nicola Scafuri and Ana Caballero

A larger number of ligands in the virtual screening library increases the chances of identifying ligands that are more potent, selective, or possess improved physicochemical properties. Mining such chemical spaces using the 3D representation of molecules has shown to be a successful approach, however, a reliable screening is only feasible when the 3D library is properly prepared. Getting ready a 3D library from a 2D representation is not trivial, since several chemical aspects must be considered, for example:

A single 2D molecule can exist in multiple protomeric and tautomeric forms at a given pH, each with a distinct distribution.
Molecules with chiral centers can exist in various 3D stereoisomeric forms.
All potential 3D conformers must be thoroughly considered.

PharmScreen^®, our field-based virtual screening software, has exhibited a promising performance in identifying novel hits within 3D libraries with similar physic-chemical properties to reference compounds. Thanks to a unique and superior 3D representation of molecules based on electrostatic, steric, and hydrophobic interaction fields derived from semi-empirical Quantum-Mechanics (QM) calculations, the internal and external benchmarks have identified a promising number of novel and diverse hits for several targets. Notwithstanding, even these encouraging results rely on the adequacy of using a 3D accurately prepared library.
We have developed an internal protocol for preparing 3D libraries suited for ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS), such as docking campaigns, and pharmacophore modeling solutions. It aims to establish a standardized and easily reproducible protocol for preparing 3D libraries.
The protocol integrates internal scripts and the PharmScreen^® software, and it was initially utilized to prepare the Enamine Screening Collection library. This library is one of the world’s largest screening compound libraries, boasting over 4.4 million unique compounds. The protocol involved the generation of different protomers and tautomers at pH 7.4, and all possible stereoisomers and conformers. As a result, our protocol ensures an extensive, high-quality chemical space, to deliver unique and tailored drug discovery solutions. Indeed, we have now one of the largest and most up-to-date 3D screening libraries of synthesizable compounds for early drug discovery projects, featuring:

Up to 270 million conformers for LBVS
Up to 7.9 million 3D stereoisomers for docking campaigns, optimized for pharmacophore modeling solutions
In addition, a complete screening of this library can be achieved in just 25 hours using PharmScreen^®

Our protocol can also filter the prepared 3D library based on drug-like properties to focus the hit ID towards the drug-like space, enhancing efficiency in the execution of CADD projects.
This robust protocol has shown to be extensible to other commercial libraries, for example, the Molport Screening Compounds library, further expanding the chemical space from which we can extract novel hits.

Interested in the application of QM methods to drug discovery? Pharmacelera software uses a unique 3D representation of molecules based on electrostatic, steric and hydrophobic interaction fields derived from semi-empirical QM calculations. Discover PharmScreen^®, exaScreen^® and PharmQSAR^®. Need a customized solution for your drug discovery project? Contact our team to arrange a call and discuss your current challenges.

The post A new standardized protocol for the preparation of large 3D fully enumerated compound libraries appeared first on Pharmacelera | Pushing the limits of computational chemistry.

Using Artificial Intelligence for faster Quantum Mechanical parametrization

Enric Herrero — Thu, 24 Oct 2024 09:51:56 +0000

By Carlos Cruz and Ana Caballero

The success of a ligand-based virtual screening campaign relies on their molecular descriptors, in this sense, Quantum Mechanics (QM) offer higher accuracy by capturing detailed electronic properties, the influence of 3D conformation, and key interactions that classical descriptors often overlook. Pharmacelera´s property molecular descriptors exploits self-consistent reaction ﬁelds methods to define hydrophobic topologies from atomic contributions.

Our unique descriptors are calculated using accurate QM methods, through the Recife Model 1 (RM1) parametrization of the Miertus Scrocco Tomassi (MST) model, which is known to be a reference method due to its good balance between accuracy and calculation time. However, QM calculations are still time-consuming, especially for huge chemical spaces. To handle this challenge, our team has developed a Machine Learning model for predicting atomic logP, to determine if exploring huge chemical spaces in a reduced amount of time is possible.

Our model includes physical descriptors (ex. topological, steric, and electrostatic descriptors), transferring information about the 3D environment of the atom to the model. The precise description of each atom gave us the possibility to develop and validate a model able to predict 3D atomic contributions to logP values with an R^2 >0.9 for any kind of neutral drug-like molecule 2.000 times faster than the calculations using the RM1 parametrization (see graphics (A) and (B))

Using this atomic description, we have accurately extended it to predict other atomic properties. For example, graph (C) shows the excellent results obtained for Mulliken Charges derived from Density Functional Theory calculations from the QMUGs library.

Need a customized solution for your drug discovery project? Contact our team to arrange a call and discuss your current challenges.

The post Using Artificial Intelligence for faster Quantum Mechanical parametrization appeared first on Pharmacelera | Pushing the limits of computational chemistry.

Quantum mechanical-based strategies in drug discovery

Fernando Martín — Thu, 22 Aug 2024 09:53:21 +0000

By Tiziana Ginex and Fernando Martin

The ever-increasing accessible chemical space opens the door to the search for new chemical matter for drug discovery. However, this also poses a challenge for computer-aided drug design methods. Quantum mechanical (QM) methods provide a chemically accurate description of molecular properties, albeit restricted to small size systems. The availability of high-quality QM-based descriptors implemented in refined algorithms and combined with efficient computational protocols can help to prioritize hits, avoiding the occurrence of bias artifacts in chemical library screening.

Different efforts are underway to apply accurate methods to the ever-expanding accessible chemical space: (i) the development of computationally efficient semiempirical methods as well as the calibration of multiscale QM/MM methods, (ii) the redefinition of physics-based force fields tailored to QM, suitably refined to provide an accurate description of the complex network of intermolecular interactions, and (iii) the generation of QM-assisted machine learning (ML) models.

In this review article, the authors summarize relevant advances in the application of the above QM-based methods to the characterization of bioactive species, structure-guided hit-to-lead optimization, and the identification of molecular features of bioactivity.

Read the article

Need a customized solution for your drug discovery project? Contact our team to arrange a call and discuss your current challenges.

The post Quantum mechanical-based strategies in drug discovery appeared first on Pharmacelera | Pushing the limits of computational chemistry.