Welcome to the Optibrium Community





Forgot login?
Register

Publications & Presentations

Search


html/modules.php!!

Latest Publications & Presentations


AI Advances Healthcare Research

Monday, 10 December 2018 14:23

Harnessing AI for drug discovery applications will significantly speed the identification of promising drug candidates, believes Matt Segall, CEO at Optibrium. The UK-based firm, together with partners Intellegens and Medicines Discovery Catapult, recently received a grant from Innovate UK to help fund a £1 million project focussed on combining Optibrium’s existing StarDrop software for small molecule design, optimisation and data analysis, and Intellegen’s deep learning platform Alchemite.

The aim is to develop a novel, deep learning AI-based method for predicting the ADMET (absorbtion, distribution, metabolism, excretion and toxicity) properties of new drugs candidates. Ultimately, the platform could help to guide the selection and design of more effective, safer compounds earlier in the discovery process...

 

You can link to the Scientific Computing World article here

Read more...


Managing Internal and External Chemistry for Efficient Drug Delivery

Friday, 30 November 2018 13:50

At the 2018 Streamlining Drug Discovery Symposium, USA, David Hollinshead (Elixir Software) and Andrew Griffin (Praxis Precision Medicines) presented Managing Internal and External Chemistry for Efficient Drug Delivery.

You can download the slides here as a PDF

Read more...


Electrostatic Complementarity as a New Approach to Visualize and Predict Activity

Thursday, 29 November 2018 15:54

At the 2018 Streamlining Drug Discovery Symposium, Sylvie Sciammetta presented Electrostatic Complementarity as a New Approach to Visualize and Predict Activity.

You can download her slides here as a PDF

Read more...


Medicinal Chemist’s Relationship with Additivity

Wednesday, 28 November 2018 15:12

Medicinal Chemist’s Relationship with Additivity: Are we Taking the Fundamentals for Granted?

At the San Francisco, Streamlining Drug Discovery Symposium 2018, J. Guy Breitenbucher from UCSF gave this in-depth presentation.

You can download the presentation slides here as a PDF

Read more...


Bigfoot, the Loch Ness Monster, and Halogen Bonds

Thursday, 22 November 2018 10:37

At the 2018 Streamlining Drug Discovery Symposium in San Diego, David Lawson treated us to this illuminating presentation entitled Bigfoot, the Loch Ness Monster, and Halogen Bonds: Separating Rumors from Reality in Molecular Design.

You can download his slides here as a PDF

Read more...


A Novel Scoring Profile for the Design of Antibacterials Active Against Gram-Negative Bacteria

Friday, 16 November 2018 12:15

At the 2nd SCI/RSC Symposium on Antimicrobial Drug Discovery, 12-13 November 2018, Bailey Montefiore, Optibrium - Franca Klingler, BioSolveIT - Nicholas Foster, Optibrium presented 'A Novel Scoring Profile for the Design of Antibacterials Active Against Gram-Negative Bacteria'.

Introduction

The increasing occurrence of multidrug-resistant bacteria is one of the major global threats to human health. Design of new antibacterials is challenging because new compound classes often do not possess the unique physicochemical properties required to penetrate the gram-negative cell wall. It is accepted that the physicochemical properties of many drugs are similar and attempts have been made to characterise these ‘drug-like’ properties, such as Lipinski’s ‘rule of five’ for orally dosed drugs. However, antibiotics are a known exception to these rules. We compared antibiotics active against gram-negative bacteria with other classes of drug and compounds considered in medicinal chemistry projects to determine criteria for selection of compounds with a higher chance of success as a gram-negative antibacterial. These criteria are based on calculated properties, so can help to guide the design and selection of compounds in discovery projects.

You can download the poster presentation as a PDF

Read more...


Imputation of Protein Activity Data Using Deep Learning

Wednesday, 14 November 2018 15:44

At the US Symposia, Streamlining Drug Discovery 2018 in Cambridge MA, Matthew Segall from Optibrium and Tom Whitehead from Intellegens presented Imputation of Protein Activity Data Using Deep Learning.

You can download the complete slide presentation as a PDF

Read more...


WaterSwap to Assess Target Druggability

Monday, 12 November 2018 15:07

At the 2018 Streamlining Drug Discovery Symposium in San Diego and San Francisco, Adam Kallel gave an insightful presentation on WaterSwap to Assess Target Druggability.

You can download his slides as a PDF

Read more...


Using AI to Improve the Safety of New Drug Candidates

Thursday, 08 November 2018 08:37

On 18 October 2018, at the Streamlining Drug Discovery Symposium in Cambridge MA, Nigel Greene gave this fascinating presentation on Using AI to Improve the Safety of New Drug Candidates.

You can download his slides as a PDF

Read more...


Two Decades under the Influence of the Rule of Five and the Changing Properties of Approved Oral Drugs

Monday, 29 October 2018 09:06

This paper appeared in Journal of Medicinal Chemistry, September 13, 2018.

Abstract

Two decades have passed since the rule of five ushered in the concept of “drug-like” properties. Attempts to quantify, correlate, and categorize molecules based on Ro5 parameters evolved into the introduction of efficiency metrics with far reaching consequences in decision making by industry leaders and scientists seeking to discover new medicines. Examination of oral drug parameters approved before and after the original Ro5 analysis demonstrates that some parameters such as clogP and HBD remained constant while the cutoffs for parameters such as molecular weight and HBA have increased substantially over the past 20 years. The time dependent increase in the molecular weight of oral drugs during the past 20 years provides compelling evidence to disprove the hypothesis that molecular weight is a “drug-like” property. This analysis does not validate parameters that have not changed as being “drug-like” but instead calls into question the entire hypothesis that “drug-like” properties exist.

Read more...


Antimalarial Lead-Optimisation Studies on a 2,6-Imidazopyridine Series within a Constrained Chemical Space To Circumvent Atypical Dose−Response Curves against Multidrug Resistant Parasite Strains

Wednesday, 24 October 2018 07:58

This paper appeared in Journal of Medicinal Chemistry, September 26, 2018.

Abstract

A lead-optimization program around a 2,6-imidazopyridine scaffold was initiated based on the two early lead compounds, 1 and 2, that were shown to be efficacious in an in vivo humanized Plasmodium falciparum NODscidIL2Rγnull mouse malaria infection model. The observation of atypical dose–response curves when some compounds were tested against multidrug resistant malaria parasite strains guided the optimization process to define a chemical space that led to typical sigmoidal dose–response and complete kill of multidrug resistant parasites. After a structure and property analysis identified such a chemical space, compounds were prepared that displayed suitable activity, ADME, and safety profiles with respect to cytotoxicity and hERG inhibition.

Read more...


High-Quality Hits from High-Throughput Screens

Monday, 15 October 2018 13:21

This paper appeared in Genetic Engineering & Biotechnology News, October 15, 2018.

Abstract

When analysing the results from a high throughput screening (HTS) campaign the goal is to identify diverse hit series with high activity, structure-activity relationships (SAR) that indicate the opportunity for further optimisation and good ‘lead like’ properties. The common practise is to apply filters to these large datasets, for example an activity threshold or simple properties such as molecular weight, logP, numbers of hydrogen bond donors and acceptors or the presence of substructures that may indicate non-specific binding. However, this process draws artificially harsh distinctions between compounds, given the inherent variability in HTS data and the low correlation between simple properties and the ultimate in vivo disposition of a compound. This leads to selection of ‘false positives’, i.e. active compounds that are not good starting points for further optimisation and ‘false negatives’, i.e. potentially good compounds that have been inappropriately rejected. We will illustrate how a true multi-parameter approach enables appropriate weight to be given to these data to confidently identify high quality, potent hits while avoiding missed opportunities.

Mapping this information across the chemical diversity of the compounds explored in an HTS campaign, by clustering or visualisation of a ‘chemical space’, helps to find ‘hot spots’ representing high quality series of compounds for further investigation while also considering diverse chemistries to provide potential backup series. Finally, exploring the SAR within these series then helps to identify further opportunities for optimisation. We will show how this can all be achieved in a high visual and intuitive way, to move quickly and confidently from initial HTS hits to high quality lead series.

Read more...


Hydrogen Bonding: Ab Initio Accuracy From Fast Interatomic Gaussian Approximation Potentials

Thursday, 23 August 2018 12:26

Mario Öeren gave this presentation at the ACS Fall 2018 National Meeting & Exposition held in Boston, USA.

Abstract

Non-covalent, electrostatic interactions play a significant role in many chemical applications and evaluating their strength is crucial for progress in fields such as drug design and material science. In most cases, due to the nature of these interactions, ab initio calculations are required to accurately assess their strength. However, due to their computational cost, ab initio methods are not suitable for screening datasets with large numbers of structures.

We will present a method based on Gaussian approximation potentials (GAPs), which are interatomic potentials trained on ab initio data using machine learning. While GAPs could be applied to any interaction, we chose hydrogen bonds as an example for this presentation. We will describe the workflow to prepare the GAP training set, how to generate GAPs from density functional theory data using the software QUIP and how to calculate the hydrogen bond energies for a structure from the resulting model. Such an approach allows us to achieve results close to ab initio accuracy, but with significantly lower computational costs. The results are validated against the ab initio calculations and quantum theory of atoms in molecules results.

While GAPs have been mostly used for molecular dynamics simulations of bulk crystals, they can be applied to a variety of problems which require the exploration of a complex potential energy surface (PES); for example, the hydrogen bond energy model described herein can be used in scoring functions for protein-ligand interactions.

Read more...


Robotic Drug Discovery: An Automated Design and Synthesis System to Boost SAR Investigations

Tuesday, 19 June 2018 17:20

Dr Tsukasa Ishihara, National Institute of Advanced Industrial Science and Technology (AIST), gave this presentation at the "Streamlining Drug Discovery" symposium held in Tokyo, Japan on 5 June 2018.

Abstract
We propose an innovative automated architecture to accelerate drug discovery. The system consists of computational drug design programs integrated with robotic compound synthesis apparatus. The computational programs design potentially novel candidates based on tacit knowledge which is automatically extracted from tens of thousands of papers in the medicinal chemistry field, and predict their profiles based on the state-of-the-art machine learning technologies including deep learning. Flow reactors are a key operation device integrated with preparative chromatography to synthesize a series of analogous molecules. Our system has elucidated novel potent compounds comparable to a clinical candidate.

You can download this presentation as a PDF.

Read more...


Library Design for Collaborative Drug Discovery: Expanding Druggable Chemogenomic Space

Tuesday, 19 June 2018 17:17

Dr Kazuyoshi Ikeda, Keio University, gave this presentation at the "Streamlining Drug Discovery" symposium held in Tokyo, Japan on 5 June 2018.

Abstract
Drug discovery data has dramatically increased in the past 20 years due to chemical genomics and automation of screening methods. As a result, we know that chemical space is still immense, and thus it is important to find lead compounds efficiently in the early phases by utilizing information from past drug discovery projects. Recently, collaborative approaches using in silico methods have been successful in expanding the druggable chemical space of screening libraries and identifying hit/lead compounds. Our group (Keio Univ) is constructing an informatics system for analyzing diversity of the compound library collected from a drug discovery screening consortium in Japan. During the talk I will outline examples of how we utilise recent informatics technologies to efficiently design chemical libraries and identify targets.

You can download this presentation as a PDF.

Read more...


A Practical View of Structure Activity Relationship (SAR) Analysis in Novartis Shanghai

Thursday, 14 June 2018 09:43

Dr Zhengtian Yu and Dr Sean Xiao, Novartis, gave this presentation at the "Streamlining Drug Discovery" symposium held in Shanghai, China on 31 May 2018.

Abstract
Structure Activity Relationship (SAR) is the relationship between a chemical structure and its biological activities. SAR analysis is widely used in different stage of drug discovery for desired pharmacological and therapeutic activities. This talk will introduce various tools we use for SAR analysis in CNIBR (Novartis Shanghai), and their using scenarios, as well as an example of a patent analysis workflow, during which use of StarDrop is discussed.

You can download this presentation as a PDF.

Read more...


In Silico Approaches in Genetic Toxicology: Progress and Future

Thursday, 14 June 2018 09:35

Dr Masamitsu Honma, National Institute of Health Science, gave this presentation at the "Streamlining Drug Discovery" symposium held in Tokyo, Japan on 5 June 2018.

Abstract
Currently, more than 130 million chemical substances have been registered in the CAS registry, and this number is increasing at a rate of 4,000/day. Among these chemicals, approximately 120,000 are industrially produced and exist in our environment. To cover the huge number of chemical substances that affect human health, effective screening tools are required. In silico and quantitative structure–activity relationship (QSAR) models defining toxicological endpoints are desirable for regulatory authorities to identify chemicals causing adverse effects without conducting actual toxicological studies.

There has been considerable effort in the development of QSAR models to predict mutagenicity among many toxicological endpoints because mutagenic chemicals pose the highest concern for human health. The recently developed ICH-M7 guideline (Assessment and control of DNA-reactive impurities in pharmaceuticals to limit potential carcinogenic risk) allows the use of the in silico approach to predict Ames mutagenicity for initially assessing impurities in pharmaceuticals. This is the first international guideline addressing the use of QSAR models in lieu of an actual toxicological study for human health assessment. QSAR models for the Ames assay now require higher prediction power to definitely capture mutagenic chemicals. To increase the prediction power, experimental data sets required to build the models are important. A large number of highly reliable data sets are essential to allow the development and improvement of QSAR models. DGM/NIHS in Japan has the largest Ames mutagenicity database, containing approximately 12,000 new chemicals that have not been previously used for developing QSAR models. We provided the Ames data to vendors to improve their QSAR models. The Ames/QSAR international collaborative project, together with 12 QSAR vendors, started in 2014 and has recently been completed. All QSAR models have considerably improved. Some QSAR models showed nearly 90% prediction power, which is the same level as that of the inter-laboratory correspondence of the Ames assay. Using the in cerebro (expert judgement) approach, we can further predict the relevance of mutagenicity data in humans. We are approaching a new era wherein “in silico/in cerebro” will replace “in vitro/in vivo” in genetic toxicology.

You can download this presentation as a PDF.

Read more...


Theoretical Studies of G-Protein-Coupled Receptors

Thursday, 14 June 2018 09:29

Dr Xianqiang Sun, Guangzhou Medical University/ Wuxi Apptec, gave this presentation at the "Streamlining Drug Discovery" symposium held in Shanghai, China on 31 May 2018.

Abstract
The family of G-protein-coupled receptors (GPCRs) contains the largest number of drug targets in the human body, with more than a quarter of the clinically used drugs targeting them. Because of the important roles GPCRs play in the human body, the mechanisms of activation of GPCRs or ligands binding to GPCRs have captivated a great deal of research interest since their discovery. A number of GPCR crystal structures determined in recent years have provided us with unprecedented opportunities to investigate how GPCRs function through the conformational changes regulated by their ligands. This has motivated me to perform molecular dynamics (MD) simulations in combination with a variety of other modeling methods to study the activation of some GPCRs and their ligand selectivity. To address these two issues, we are using opioid receptor, β2 Adrenergic receptor and corticotropin-releasing factor receptors as example to address these two issues and interesting results will be listed in my presentation.

You can download this presentation as a PDF.

Read more...


Genetic Toxicology: Progress on International Test Guidelines and New Methods

Thursday, 14 June 2018 09:24

Dr Yan Chang, National Shanghai Center for New Drug Safety Evaluation and Research, gave this presentation at the "Streamlining Drug Discovery" symposium held in Shanghai, China on 31 May 2018 .

Abstract
Genotoxicity tests are conducted to determine if a chemical or physical agent has the potential to cause mutations or chromosomal damage which, in turn, may lead to adverse health consequences, including cancer, reproductive impairment, developmental anomalies, or genetic diseases. The International Conference on Harmonisation (ICH) S2(R1) guideline (2011) recommends two standard regulatory test batteries, generally includes an assessment of genotoxicity in bacterial and/or mammalian cells in vitro together with rodent assays for chromosomal and/or DNA damage. Several test guidelines of the Organization for Economic Co-operation and Development (OECD) for evaluating the genotoxicity of chemicals and pharmaceuticals have been updated in 2016. The China FDA finalized the revision of the guidance on Genotoxicity Testing and Data Interpretation for Pharmaceuticals this March.

In addition to the current test battery, it was also essential to focus on those technologies most likely to provide an adjunct to, or advantage over, current methods used to predict in vivo genotoxicity and/or carcinogenicity activity to improve human risk assessment. In vivo liver micronucleus test, Pig-a gene mutation assay and humanized in vitro genotoxicity test systems are being developed or in process of inter-lab validation.

You can download this presentation as a PDF.

Read more...


Capturing and Applying Knowledge to Guide Compound Optimisation

Thursday, 14 June 2018 09:17

Ed Champness, gave this presentation at the "Streamlining Drug Discovery" symposia held in Shanghai, China on 31 May 2018 and Tokyo, Japan on 5 June 2018.

Abstract
Compound design requires a combination of knowledge and expertise from different perspectives: Understanding of structure-activity relationships (SAR), based on data from previously studied compounds; expertise from diverse fields to define the multi-parameter optimisation (MPO) objectives of a project; and knowledge of synthetic strategies that may be applicable to create the next rounds of compounds for investigation. All of these forms of knowledge can be captured and applied computationally: Machine learning methods can generate quantitative structure-activity relationship (QSAR) models to predict the properties of novel, virtual compounds; MPO methods capture the desired property criteria for a successful compound for a specific project and rigorously prioritise ideas for consideration; and, optimisation strategies can be captured as structural transformations that reflect steps made in previous chemistry projects.

In this presentation, we will describe these methods and illustrate how they can be seamlessly combined to rigorously explore new, relevant compound ideas and prioritise those most likely to achieve a project objective. This approach can help to stimulate the search for new optimisation strategies and explore a much broader range of compounds than could be achieved based on a single chemist’s or even a project team’s experience. Example applications include the optimisation of compounds with a desired polypharmacology or selectivity profile and exploration of lead hopping strategies to overcome pharmacokinetic issues, while maintaining target potency.

You can download this presentation as a PDF.

Read more...


Latest Forums

Read more >