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

Pharmacelera and eMolecules Launch Integrated Solution for Faster Hit Discovery

Enric Herrero — Mon, 13 Oct 2025 15:05:04 +0000

California, US & Barcelona, Spain — October 13^th, 2025 —

Together with eMolecules we announce the launch of a new integrated solution combining ExaScreen® with the eMolecules eXplore/Synple library. The partnership delivers researchers a streamlined path from virtual screening to physical compounds, accelerating early-stage drug discovery.

The pharmaceutical industry increasingly relies on large, diverse libraries to identify novel hits. Yet, efficiently navigating chemical space while ensuring synthetic feasibility remains a key challenge. The new solution combines Pharmacelera’s Quantum-Mechanics (QM) and Machine Learning (ML)-driven algorithms with the eXplore/Synple library’s curated, synthetically tractable compounds, enabling scientists to quickly identify, source, and test new chemical matter:

Accurate screening of tractable chemical space using ExaScreen®
Rapid access to compounds for experimental validation via eMolecules
Novel IP opportunities through exploration of untapped chemical diversity
End-to-end workflow from computational predictions to physical samples

“Our collaboration with Pharmacelera brings a powerful combination of computational precision and practical compound access,” said Jeff Desroches, SVP Corporate Development.

“Partnering with eMolecules aligns perfectly with Pharmacelera’s strategy of working with leading organizations that complement our technology and expertise,” said Rémy Hoffmann, Chief Business Development Officer at Pharmacelera.

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About eMolecules

eMolecules is driven to improve the human condition by enabling scientists to accelerate their research to find effective therapeutics. To achieve this, eMolecules provides business intelligence data and integrated ecommerce software for screening compounds, chemical building blocks and primary antibody supply chains. These tools, combined with their acquisition, aggregation and analytical services, greatly empower drug discovery researchers working in the pharmaceutical, biotechnology, academia, CRO and agrichemical industries. eMolecules was founded in 2005 at its San Diego, California, USA headquarters and has offices and laboratory space in San Diego and London, UK, employing nearly 60 people, globally.

About Pharmacelera

Pharmacelera is a deep-tech science-first company founded by experienced drug hunters, high-performance computing engineers, and leading academic researchers. The company has developed a proprietary in-silico platform that integrates accurate 3D Quantum-Mechanics (QM) models with advanced Artificial Intelligence (AI) algorithms to design novel, diverse, and high-quality molecules from ultra-large and previously untapped chemical spaces. Pharmacelera provides access to its cutting-edge technology for HitId, H2L and LO through yearly software licenses and it also offers AI-driven drug discovery services spanning the entire small-molecule pipeline. The company has a growing customer base across Europe and the United States, including several top-tier pharmaceutical companies.

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Domainex and Pharmacelera Join Forces to Accelerate Discovery of Molecules Targeting Transmembrane Proteins

Enric Gibert — Wed, 27 Aug 2025 07:13:33 +0000

Cambridge, UK & Barcelona, Spain — August 27^th, 2025 — Domainex, a leading integrated drug discovery services company, and Pharmacelera, a pioneer in AI-based molecular modelling technologies, today announced a strategic collaboration to support discovery programmes focused on transmembrane proteins such as G protein-coupled receptors (GPCRs) and ion channels—critical therapeutic targets implicated in a broad range of diseases.

Transmembrane proteins represent over 60% of current drug targets but pose significant challenges for drug discovery due to their structural complexity and instability outside the lipid membrane environment. By combining their complementary technologies and expertise, Domainex and Pharmacelera aim to overcome these hurdles and accelerate the discovery of novel, high-quality drug candidates.

Under the partnership, Domainex will contribute its Polymer Lipid Particle (PoLiPa) technology (which stabilises membrane protein targets by encapsulating them in polymer nanodiscs, allowing purification in their native state for screening), alongside its Direct-to-Biology (D2B) platform for high-throughput synthesis and biological testing. Pharmacelera will integrate its state-of-the-art AI-driven platforms—exaScreen® and PharmScreen®—for advanced molecular modelling, virtual screening, and library design. Together, the companies will offer a comprehensive and seamless solution to identify and optimise hits against these challenging targets.

“This collaboration between Domainex and Pharmacelera combines innovative technologies and represents optimal approaches for drug discovery programmes and is part of our commitment to achieve faster and successful approvals for our customers.” said Dr Hayley French, CEO of Domainex.

“We are excited to partner with Domainex,” said Dr. Enric Gibert, CEO of Pharmacelera. “This partnership reflects our shared commitment to advancing science and bringing impactful therapeutics in challenging areas. By pairing our cutting-edge AI models with Domainex’s experimental data and technology platforms, we can accelerate the path from concept to candidate.

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About Domainex

Domainex is a multi-award-winning, integrated drug discovery service partner which provides tailored discovery solutions from target expression through to the identification of pre-clinical candidates. Our world-leading experts accelerate research projects by combining a problem-solving approach with cutting-edge technologies such as PoLiPa and D2B. With deep expertise across a wide range of target classes and therapeutic areas, and a core focus on the hit identification and hit-to-lead stages, we deliver high-quality results that support confident, timely decision-making for our partners. Domainex operates from state of the art facilities in the Cambridge area, UK. The company has also expanded internationally and has opened an office in Cambridge, MA, to service the thriving biotechnology industry in North America. Further information about Domainex and our award-winning lead discovery services can be found at www.domainex.co.uk.

About Pharmacelera

Pharmacelera develops advanced computational tools for the discovery of novel hits using accurate Quantum-Mechanics (QM), Artificial Intelligence (AI) and High-Performance Computing (HPC). The company’s products PharmScreen®, exaScreen® and PharmQSAR use 3D molecular descriptors derived from Quantum-Mechanics (QM) calculations to mine an unexplored chemical space and to identify hits uncovered by traditional algorithms. Pharmacelera is a private company founded in 2015 and based in Barcelona, Spain. The company works with several big pharma and biotech organizations across Europe and the United States.

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Pharmacelera and UMass Chan Medical School Join Forces in Drug Discovery

Enric Gibert — Mon, 14 Jul 2025 13:13:05 +0000

We are thrilled to announce a collaboration between Pharmacelera and the UMass Chan Medical School aimed to support early-stage drug discovery projects.

Through this collaboration, Pharmacelera will use its cutting-edge computational chemistry technology, AI-driven molecular modelling platforms and industry-based expertise in medicinal chemistry to support several early-stage drug discovery programs targeting different families of targets with the aim of enhancing hit identification, lead optimization, and chemical space exploration.

“This partnership reflects our shared commitment to advancing science and bringing impactful therapeutics closer to the clinic,” said Enric Gibert, CEO of Pharmacelera. “Our technology is designed to push the frontiers of early drug discovery, and we are proud to support UMass Chan’s world-class research teams.”

“We are excited to partner with Pharmacelera to integrate their advanced modelling tools into our drug discovery workflows,” said Huseyin Mehmet, Executive Director, New Ventures, BRIDGE Innovation & Business Development of the UMass Chan Medical School. “This collaboration will enhance our ability to discover and optimize novel compounds for unmet medical needs in the areas of cancer and ALS.”

Stay tuned as we share more about our joint projects and scientific milestones in the coming months.

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About UMass Chan Medical School

UMass Chan Medical School, one of five campuses of the University of Massachusetts system, comprises the T.H. Chan School of Medicine, the Morningside Graduate School of Biomedical Sciences, the Tan Chingfen Graduate School of Nursing, ForHealth Consulting at UMass Chan Medical School, MassBiologics, and a thriving Nobel-Prize-winning biomedical research enterprise. UMass Chan is advancing together to improve the health and wellness of our diverse communities throughout Massachusetts and across the world by leading and innovating in education, research, health care delivery and public service. It is ranked among the best medical schools in the nation for primary care education and biomedical research by U.S. News & World Report. Learn more at www.umassmed.edu.

About Pharmacelera

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General Inception and Pharmacelera Partner to Advance Drug Discovery with Exponential Screening Capabilities

Fernando Martín — Tue, 09 May 2023 14:27:06 +0000

3D quantum-mechanics, molecular descriptors, and artificial intelligence provide unique capabilities in the chemical space to identify and qualify novel hits

PALO ALTO, Calif and BARCELONA, Spain: General Inception (GI), a global company Igniter, and Pharmacelera, a Barcelona-based deep-tech company, announced its partnership to use exaScreen® for novel drug discovery. The platform technology, developed by Pharmacelera, enables exponential screening capabilities of in-silico compound libraries, increasing the exploration of chemical space to identify hits. Traditional technologies can only mine about 10 million diverse molecules versus exaScreen®’s over 30 billion. The collaboration will focus on the application of exaScreen®’s state-of-the-art virtual screening technology for General Inception’s therapeutics companies looking for novel, diverse and synthesizable hits. exaScreen® uses accurate 3D Quantum-Mechanics (QM) molecular descriptors and Artificial Intelligence (AI) to mine efficiently a humongous chemical space and potentially boost the Intellectual Property (IP) of drug discovery projects.

“This is a step forward in our aim to retrieve and discover new chemical matter, which is a fundamental pillar in early drug discovery,” said Dr. Venkat Reddy, Chief Scientific Officer of General Inception. “Pharmacelera has already demonstrated that their proprietary technology is able to identify novel yet feasible scaffolds that are totally missed by traditional screening methodologies. We look forward to applying this technology and believe it will provide a meaning benefit to the drug discovery efforts of our companies.”

“We are very excited to establish this collaboration with General Inception, a fast-growing company Igniter in the United States and Europe,” said Dr. Enric Gibert, Pharmacelera’s CEO. “We appreciate getting in on the ground-floor of collaborations to best leverage the power of exaScreen®. GI’s business model enables us to reach a diverse group of innovative companies at the start of their journey and collaborate with pharma executives to resolve key challenges in drug discovery.”

ABOUT GENERAL INCEPTION

General Inception is pioneering company creation as an Igniter company. General Inception partners with extraordinary scientific founders at the inception of their journey to efficiently translate their groundbreaking innovations into transformational companies that address humanity’s grand challenges. As a business co-founder, GI brings together domain and functional expertise, executive talent, infrastructure and development resources, and capital to ignite, nurture and scale the company journey.

For more information, please visit www.generalinception.com

ABOUT PHARMACELERA

Pharmacelera develops advanced computational tools for the discovery of novel hits using accurate Quantum-Mechanics (QM), Artificial Intelligence (AI) and High-Performance Computing (HPC). The company’s products use 3D molecular descriptors to mine an unexplored chemical space and to identify hits uncovered by traditional algorithms. Pharmacelera is a private company founded in 2015 and based in Barcelona, Spain. The company works with several big pharma and biotech organizations across Europe and the United States.

For more information, please visit www.pharmacelera.com and www.pharmacelera.com/exascreen/

CONTACTS:

General Inception

Rebecca Galler – rebecca.galler@generalinception.com

Pharmacelera

Rémy Hoffmann – remy.hoffmann@pharmacelera.com

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DyNAbind and Pharmacelera engage in a research collaboration to apply Artificial Intelligence to DNA-Encoded Library screening

Fernando Martín — Fri, 01 Apr 2022 07:14:05 +0000

DyNAbind and Pharmacelera are happy to announce a joint scientific collaboration in the fields of DNA-Encoded Libraries (DEL) and Artificial Intelligence (AI) for drug discovery. DEL screening is perceived as one of the main sources of new chemical matter in forthcoming years as the technology enables testing millions of molecules in a single wet experiment, in contrast to traditional High-Throughput Screening (HTS) methodologies.

Some of the main challenges of DEL screenings are the management of noisy data as well as the conversion of binders into easily synthesizable or purchasable hits. Given the big amount of datapoints provided by DEL screening, AI and Ligand-Based Drug Discovery (LBDD) in-silico tools are attractive tools for overcoming these limitations. The conjunction of both companies’ technologies will enable pharmaceutical companies, biotech organizations and public research institutions to find novel chemical scaffolds with new Intellectual Property (IP) from a huge and unexplored chemical space.

“We are excited to begin this scientific collaboration with Pharmacelera and to assess the benefits of our complementary technologies for drug discovery“, says Michael Thompson, DyNAbind’s co-founder and CEO. “Pharmacelera’s accurate 3D molecular descriptors and expertise in AI can help in the post-processing of the data and in overcoming our main challenges”, he adds.

“DyNAbind is a premier organization in the area of DNA-Encoded Libraries, and we are very excited to kick-off a collaboration with them“, Enric Gibert, CEO of Pharmacelera explains. “DyNAbind’s DEL capabilities, which combine traditional small molecule approaches with fragment-based data, generate millions of useful and meaningful datapoints that our state-of-the-art LBDD tools and AI expertise can use to propose novel hits with larger chemical diversity”, he adds.

DyNAbind is a privately owned company focusing on novel DNA-Encoded Library approaches for drug discovery. The company combines small molecule and fragment-based approaches with unprecedented levels of library QC to offer more relevant medicinal chemistry start points in a highly drug-like space. A patented decoding system and powerful in-house informatics platform allows deep insight into the screening data for supporting either de novo drug design or optimization of existing ligands. Since foundation in 2017, DyNAbind has been based in Dresden, Germany and collaborates with pharmaceutical companies, biotechs and academic groups in the US, Europe and Asia.

Pharmacelera develops advanced computational tools for the discovery of novel hits using accurate Quantum-Mechanics (QM), Artificial Intelligence (AI) and High-Performance Computing (HPC). The company’s products PharmScreen and PharmQSAR use 3D molecular descriptors to mine an unexplored chemical space and to identify hits uncovered by traditional algorithms. Pharmacelera is a private company founded in 2015 and based in Barcelona, Spain. The company works with several big pharma and biotech organizations across Europe and the United States.

See press release at Dynabind.com

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Computational Chemistry in Blood Brain Barrier penetration for CNS Drug Discovery

Enric Herrero — Tue, 09 Mar 2021 09:50:32 +0000

By Giorgia Zaetta – March. 9, 2021

The challenges of CNS Drug Discovery

In the last decades, few clinically active drugs for brain disorders were identified, and many research projects have been abandoned because of drawbacks in the drug discovery pipeline. One of the main CNS drug discovery challenges, is to understand the mechanisms that govern blood–brain barrier permeability. This is mainly due to the physical restriction of the membrane as well as the activity of efflux transporters present, that create an obstacle to drug delivery into the brain. Therefore, it is significantly important to characterize the BBB permeability of small molecule as early as possible.

What is the Blood Brain Barrier?

The blood–brain barrier (BBB) describes the endothelial layer of cells that surrounds the central nervous system (CNS), and is identified as a structural and chemical barrier between the brain and systemic circulation. BBB is a special selective semi-permeable membrane, with physiological role of preventing harmful substances from entering the brain.

The illustration of the blood-brain barrier (From: Liu, et al. 2012)

Passive Diffusion and Active Transport

BBB permits lipid-soluble small molecules to cross by passive diffusion, and allows the selective active transport of molecules, such as glucose and some amino acids, via the P-glycoprotein-mediated active transport mechanism (P-gp). A drug’s BBB permeability profile is influenced by multiple component that contribute positively or negatively to its ability of getting in the brain:

Molecular Weight up to 400 Da
Molecular Volume (Fisher et al. found a 100-fold decrease in penetration between molecules with volumes of 100 Å compared to 50 Å)
Hydrophilicity, as major determinant of permeability

Active transport by the BBB’s efflux pumps is equally important to obstacle diffusion of drugs to the brain, and some small molecules can be found as P-gp substrates.

Mechanisms of drugs passing BBB: the right part presents the blood vessel, which shows the mechanisms for drug passing BBB, and the left part is the brain.

The importance of crossing the BBB in Drug Discovery

Many active compounds faced CNS research discontinuation due to low BBB penetration rather than lack of potency; therefore, to determine whether a drug has BBB permeability is a key pre-requirement of discovering CNS drugs.

The limitations

Ways of testing BBB compound’s penetration are considered quick, high throughput and reliable. PAMPA-BBB (the BBB parallel artificial membrane permeation assays) has been extensively used as the in vitro model of choice for predicting passive BBB permeation. In vivo tests run on primary cultures has an important ethical implication, and some have started developing 3D tissue models of the human brain endothelium that mimics BBB and can potentially substitute the current in vivo models. Even if this could possibly overcome the costs and the ethical issues with animal testing, more solutions need to be found.

One solution: Computational Chemistry

In the drug market, the cost of drug discovery failure in late stages is of high impact. The complexity of the laws regulating the use of animal samples for experimental purposes, together with the current scenario where progresses are made only in small molecules that penetrate the BBB with passive diffusion, have clearly some responsibility in the most common drawbacks.

In the future, computational methods can surely significantly improve the prediction accuracy of drug BBB permeability, and it can ultimately help drug discovery to reduce time and cost in finding new CNS drugs. For example, in 2018, Wang et al. proposed a Silico method which, combined with Machine learning, can improve this prediction.

At Pharmacelera, we are closely working on future perspective for CNS therapies, using a unique approach derived from semi-empirical Quantum-Mechanics (QM) calculations. Such fields describe with high accuracy the molecular descriptors that can determine BBB penetration.

Do you want to find out more about how Pharmacelera can help you in your CNS drug discovery project? Let us help you! Our consultants will provide you with customized solutions, always working side by side with your team. Contact us: contact@pharmacelera.com

Bibliography

“Blood–brain barrier: mechanisms governing permeability and interaction with peripherally acting μ-opioid receptor antagonists.” Viscusi, E. R. et al., Reg Anesth Pain Med 2020; 0: 1–8.

“Improved Classification of Blood-Brain-Barrier Drugs Using Deep Learning.” Miao, R. et al., Scientific Reports, 2019, 9: 8802

“A focused library of psychotropic analogs with neuroprotective and neuroregenerative potential.” Uliassi, E. et al., ACS Chem. Neurosci. 2018

“Blood-brain barrier permeation: molecular parameters governing passive diffusion.” Fischer, H. et al., J Membr Biol 1998; 165: 201–11.

“In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods.” Wang, Z. et al., ChemMedChem, 2018, 13, 2189–2201.

“Drug transport across the blood-brain barrier.” Pardridge, W. M. et al., J Cereb Blood Flow Metab 2012; 32: 1959–72.

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Are you considering tautomerism, ionization and chirality when identifying new hits?

Enric Gibert — Wed, 17 Jan 2018 14:41:51 +0000

Tautomerism, ionization and chirality are important factors to consider when building a compound library or when finding new hits.

Tautomerism and ionization

The interactions between a ligand and a target protein can be significantly affected as a result of tautomerism and ionization, potentially having a direct impact when identifying new hits for a given receptor. Hence, the enumeration of the tautomeric and protonation (ionization) states are an important step in in-silico drug discovery tasks such as virtual screening.

Tautomers are isomers differing only in the positions of hydrogen atoms and electrons. Even a simple molecule can have several different tautomeric forms. Moreover, acid/base equilibrium, which explores different protonation states by assigning formal charges to those chemical moieties that are likely to be charged (e.g., phosphate or guanidine) under different conditions, produces additional forms called protomers.

Many factors can inﬂuence the tautomeric and protonation equilibriums, such as concentration, temperature, and pH. Tautomers and protomers differ in shape, functional groups, surface, and hydrogen bonding. Therefore, tautomerism and protonation may result in alternative binding modes with the corresponding impact on ligand/protein interactions.

For instance, Polgar and co-workers have studied the impact of ligand protonation on virtual screening against BACE1 [1]. As an additional proof of the importance of these factors, the widely used ZINC database is processed to generate relevant tautomers and protomers between pH 5 and 9.5.

However, a lot of works do not consider these aspects when building databases or when performing virtual screening due to the perceived underlying complexity.

Chirality

In addition, chirality is also a crucial factor in drug discovery. The presence of an asymmetric carbon atom (chiral carbon) causes two stereoisomers (non-superposable mirror images of each other), which can show a remarkable difference in the effect of their biological action.

For example, ephedrine has been used for asthma, whereas its enantiomer, pseudoephedrine, is a nasal/sinus decongestant.

Ephedrine on the left side which is the (S) isomer and pseudoephedrine on the right side which is the (R) isomer

As other examples related to chirality, only the (S) isomer of ibuprofen is effective, whereas the (R) isomer has no anti-inflammatory action and the antihypertensive drug methyldopa owes its effect exclusively to the (S) isomer.

Ibuprofen on the left side and methyldopa on the right side which both are (S) isomers.

In conclusion, tautomerism, ionization and chirality are factors that affect the interactions between a ligand and a target protein and they should be handled properly in in-silico drug design projects.

[1] Tímea Polgár, Csaba Magyar, István Simon, and György M. Keserü. “Impact of Ligand Protonation on Virtual Screening against β-Secretase (BACE1)”. Journal of Chemical Information and Modeling 2007 47 (6), 2366-2373. DOI: 10.1021/ci700223p

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Alignment of PIM-1 Inhibitors with PharmScreen

Enric Gibert — Wed, 06 Dec 2017 11:13:02 +0000

Pim-1 is an oncogene-encoded serine/threonine kinase. Originally identified in Maloney murine leukaemia, it is involved in several cellular functions associated with survival an proliferation which confers a selective advantage during tumorigenesis [1,2]. Given this implication, it has been used as a cancer drug target [3].

IYZ and LY2 are two bioactive inhibitors of Pim-1. The main described interactions between the protein and these molecules are hydrophobic. This can be appreciated in the reference molecule in the picture below (blue residues: Lys67, Asp186, Val52, Ile185, Leu44, Phe49, Leu174, Ala65), which also shows one hydrogen bond interaction (orange residue: Lys67).

Tools using hydrophobic parameters derived from solvation models, such as a quantum mechanical (QM) version of the MST continuum method used in PharmScreen, favours this type of ligand-target interactions.

The similarity-property principle suggests that analogous compounds will likely share similar biological properties. Indeed, defining the adequate properties that define the biological interactions are fundamental to explore similarity studies. In this case, hydrophobicity is an essential interaction to be considered when a ligand-based drug design process is performed.

Alignment

In order to verify it, we have aligned IYZ against LY2 using both traditional interaction fields and PharmScreen´s hydrophobic interaction fields and the results have been compared with the crystal structure.

The picture below shows the alignment of both approaches with respect to the reference molecule in purple.When comparing this with the crystallized molecule, the alignment performed considering traditional interaction fields misses the correct pose of the molecule, while PharmScreen is able to find the bioactive overlay using hydrophobic interaction fields, as shown in the picture below.

Hence, when searching for new potential hits in ligand-based in-silico approaches, it is crucial to use models for molecular alignment and similarity that use hydrophobic properties in situations in which hydrophobicity dominates the interaction between a ligand and a protein, as the one shown in this example.

[1] C. J. Saris, J. Domen, and A. Berns, “The pim-1 oncogene encodes two related protein-serine/threonine kinases by alternative initiation at AUG and CUG.,” EMBO J., vol. 10, no. 3, pp. 655–64, Mar. 1991.

[2] J. J. Gu, Z. Wang, R. Reeves, and N. S. Magnuson, “PIM1 phosphorylates and negatively regulates ASK1-mediated apoptosis.,” Oncogene, vol. 28, no. 48, pp. 4261–71, Dec. 2009.

[3] Y. Tursynbay, J. Zhang, Z. Li, T. Tokay, Z. Zhumadilov, D. Wu, and Y. Xie, “Pim-1 kinase as cancer drug target: An update.,” Biomed. reports, vol. 4, no. 2, pp. 140–146, Feb. 2016.

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Are medicinal chemistry CRO complementing their technology with computational chemistry?

Enric Gibert — Wed, 22 Nov 2017 14:31:17 +0000

Computational chemistry has been embraced by drug discovery as a key complementary technology to medicinal and organic chemistry [1]. For instance, several big pharmaceutical companies arrange research divisions into smaller groups of cross-disciplinary researchers including computational chemists as primary stakeholders operating in a biotech environment [2].

Medicinal chemistry CROs and computational chemistry

What is the situation in small and medium Contract Research Organizations (CRO) with a deep expertise in medicinal chemistry? Some of these CROs have already built computational chemistry efforts in-house or have established collaborations with Computer-Aided Drug Design (CADD) service providers, while some are still reluctant to explore this field. The main reason is unfamiliarity, which often raises two key questions:

What are the benefits of adding computational chemistry to a drug discovery project?
Will computational chemistry over-take medicinal chemistry?

There are multiple benefits to consider:

Computational chemistry can be used to move High-Throughput Screening (HTS) assays from a brute force approach to guided experiments, reducing HTS costs by up to 1,500X as shown by ex-Pharmacia (now Pfizer) [3].
Synthesis can also be sped up by allowing medicinal and organic chemists to concentrate on fewer compounds [4] since in-silico models have shown successful use in qualifying hits from HTS [5].
CADD tools allow fast and inexpensive access to different virtual libraries that increase the chances of exploring a wider chemical space.

All the aforementioned examples are translated into the design and optimization of more robust leads.

The answer to the second question is an outright “no”: computational chemistry cannot substitute medicinal chemistry know-how. Computational chemistry provides more insights into ligand-target interactions and this information is very useful for medicinal chemists in the creative process of lead design and optimization.

Collaboration models

There are several ways in which a medicinal chemistry CRO can use CADD as complementary technology: It may either build such an effort in-house or establish external collaborations with a preferred partner.

Alternatively, it may establish relationships with several partners on a per-project basis. In case of external partners, several factors need to be considered such as the technology, area of expertise and know-how, costs, trust, flexibility and geographical influence.

In summary, computational chemistry is used as a complementary technology to reduce the costs and increase the efficiency of target-to-hit, hit-to-lead and lead optimization drug discovery stages. In the new collaborative and fragmented drug discovery ecosystem [6], new opportunities arise to reduce the gap between extremely specialized organizations and consequently improve efficiency in R&D.

Have an open discussion with us!

To find out how Pharmacelera can help you to complement your technology with CADD, contact us for an open discussion.

References

[1] G. Sliwoski, S. Kothiwale, J. Meiler, E. Lowe, “Computational Methods in Drug discovery”, Pharmacological Reviews, January 2014
[2] T.J. Ritchie, I.M. McLay, “Should medicinal chemists do molecular modelling?”, feature in Drug Discovery Today, January 2012

[3] T. Doman, S. McGovern, B. Witherbee, T. Kasten, R. Kurumbail, W. Stallings, D. Connolly, B. Shoichet, “Molecular Docking and High-Throughput Screening for Novel Inhibitors of Protein Tyrosine Phosphatase-1B”, Journal of Medicinal Chemistry, April 2002

[4] S. Basak, “Chemobioinformatics: the Advancing Frontier of Computer-Aided Drug Design in the Post-Genomic Era”, Curr Comput Aided Drug Des, March 2012

[5] R. Malik, D. Bunkar, B. S. Choudhary, S. Srivastava, P. Mehta, and M. Sharma, “High throughput virtual screening and in silico ADMET analysis for rapid and efficient identification of potential PAP248-286 aggregation inhibitors as anti-HIV agents,” J. Mol. Struct., vol. 1122, pp. 239–246, Oct. 2016.

[6] S. Mignani, S. Huber, H. Tomás, J. Rodrigues, J-P. Majoral, “Why and How Have Drug Discovery Strategies Changed? What are the New Mindsets?”, Drug Discovery Today, February 2016

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RDKit conformation generation script

Enric Herrero — Wed, 20 Sep 2017 08:00:03 +0000

By Alessandro Deplano – Sep. 20, 2017

NOTE: THERE IS A NEW VERSION OF THIS SCRIPT.

VISIT THIS PAGE TO DOWNLOAD IT.

Conformer generation is one of the first and most important steps in most ligand based experiments, particularly when the ligand’s 3D structure is unknown. For example, the quality of the conformers could affect the results of virtual screening experiments.

At Pharmacelera we have written a python script to generate conformations with RDKit¹, one of the best freely available tools for conformer generation due to its accuracy reproducing experimentally determined structures and its reasonable computing requirements².
The script (Figure 1) uses RDKit functions like EmbedMultipleConfs³ and allows the generation of high quality conformers. With the usage of multiple filters this script finds the same amount of bioactive conformations than the default function but with a 57% reduction in the number of conformers.

The optimal number of conformations will vary based on molecular flexibility. It is known that the number of rotatable bonds is highly related with the size of the conformational space. Therefore, with this script users can generate either a fixed number of conformers or generate them based on the number of molecular rotatable bonds.

To establish the best relationship between rotatable bonds and the minimum number of conformers needed to find the experimentally determined structure we performed an extensive study using an AstraZeneca dataset⁴ composed by 1456 molecules with a spectrum of rotatable bonds from 0 to 13 (Figure 2).

Figure 2 highlights an exponential correlation between the number of conformers and the rotatable bond to find the crystal structure. Based on these results the number of conformers for each molecule is determined by this equation:

Conformation_number = Number_of_rotable_bonds^3

Molecular energy is another important aspect in conformer generation. In fact, molecules can exist only in some range of energy. This script allows the user to set a maximum value of energy that all molecules can differ from the one with the lowest energy. In this context we have evaluated different energy windows to find the best energy threshold that minimizes the number of conformers without losing in accuracy.

Finally, one of the most important properties of a conformer set is a proper balance between the maximum space exploration and the minimum number of conformers. Based on this assumption this script allows a RMSD-based cleaning that keeps only those conformers which differ from the others. In particular, the script performs two RMSD-based cleanings which can be used in combination or as single. The first RMSD cleaning is performed with the pruneRmsThresh option of the RDKit EmbedMultipleConfs function, which performs the cleaning before molecular minimization.Performing an RMSD cleaning before energy minimization, however, might cause that different conformers after the minimization fall into the same local energy minimum and become structurally very similar. To avoid this problem another RMSD cleaning function was added to the script which performs the purge after the minimization. Also, in this case, some experiments were performed to find the optimal RMSD cutoff value to reduce the number of conformers without influencing the accuracy of the results (Figure 3).

Strategy	Conformer Number Average	% Crystal Structures Found
Default function	122	81.18 %
Energy Cleaning 6.0 Kcal/mol	121	81.18 %
RMSD Cleaning 0.50 Å	52	80.15 %
Final Configuration: Energy Cleaning 6.0 Kcal/mol & RMSD Cleaning 0.50 Å	52	80.01 %

Table shows the amount of conformers generated with each script configuration option and the percentage crystal structures found. It can be seen that a post minimization RMSD cleaning is very useful reducing the required number of conformations. On the other hand, the energy cleaning does not show a significant impact in conformer reduction. However, it is kept in order to remove outliers with unreasonable energy levels.

http://www.rdkit.org/
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http://www.rdkit.org/Python_Docs/rdkit.Chem.rdDistGeom-module.html#EmbedMultipleConfs
Giangreco I, Cosgrove DA, Packer MJ (2013) An extensive and diverse set of molecular overlays for the validation of pharmacophore programs. J Chem Inf Model 53:852–866.

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