{"id":873,"date":"2022-05-12T17:25:33","date_gmt":"2022-05-12T15:25:33","guid":{"rendered":"https:\/\/compbat.eu\/?p=873"},"modified":"2022-05-17T12:27:18","modified_gmt":"2022-05-17T10:27:18","slug":"high-throughput-screening-methodology","status":"publish","type":"post","link":"https:\/\/compbat.eu\/2022\/05\/12\/high-throughput-screening-methodology\/","title":{"rendered":"High-throughput screening methodology"},"content":{"rendered":"

TTK<\/a> preliminary results<\/b><\/h2>\n

The TTK team is currently expanding the molecular library of redox active compounds beyond the pyridoxal database. The main aim is to build a structurally diverse set of molecules using the developed computational protocol, and apply machine learning techniques for high-throughput screening of the target 100 000 molecules.<\/span><\/p>\n

The TTK team is primarily involved in work package<\/span> WP1, which aims at developing a high-throughput screening methodology that enables the identification of promising candidates of water-soluble compounds for new generation redox flow batteries<\/span>. The group has developed an <\/span>efficient computational protocol that utilizes a combination of various electronic structure <\/span>methods and it provides high quality predictions for reduction potentials. The protocol has been <\/span>applied to build a molecular database<\/span> comprising over 6700 pyridoxal-based molecules.\u00a0<\/span><\/p>\n

Several machine learning (ML) techniques, including the commonly used <\/span>random forest algorithm as well as graph convolutional neural networks, <\/span>were applied to the pyridoxal database to assess their performance for<\/span> predicting reduction potentials and aqueous solubilities<\/span>. Most of the tested ML methods were<\/span> found to perform remarkably well, exceeding the accuracy of the<\/span> quantum chemical <\/span>computational protocol used <\/span>to generate the pyridoxal database.<\/span><\/p>\n

Some of the pyridoxal derivatives examined <\/span>computational were synthesized<\/span> by the JYU group<\/span>, but <\/span>stability problem<\/span>s were encountered in electrochemical studies. To provide insight into the stability issue, <\/span>the TTK group adopted a new computational methodology for the characterization of radical stabilities<\/span>, <\/span>and provided a detailed analysis for the entire pyridoxal molecular set (see Figur<\/span>e below). The radical stability descriptors <\/span>were found to be rather sensitive to the bulkiness of the substituent at the pyridinium N atom, as well as to electronic and steric nature of the other substituents. The radical forms of the compounds synthesized within work package WP4, were predicted to be unstable species.<\/span><\/p>\n

Current activities of the TTK team are focused on the expansion of the molecular library of redox active compounds beyond the pyridoxal database. A molecular library of viologen derivatives is being developed, and a structurally very diverse set of molecules from open access databases are compiled. <\/span>The application of the high-throughput screening methodology elaborated within CompBat will enable the screening of about 100 000 molecules.<\/span><\/p>\n

Here we include a figure that illustrates the stability analysis of pyridoxal derivatives (see below).\u00a0<\/span><\/p>\n

\"\"<\/p>\n

Figure<\/b>: Computed radical stability data for the entire set of radicals included in pyridoxal database. Color scale is used for <\/span>radical stability score (rss)<\/span><\/i> data (blue \u2013 unstable, red \u2013 stable radicals). The circled red dots in the middle-bottom refer to the compounds synthesized within CompBat.<\/span><\/div>\n","protected":false},"excerpt":{"rendered":"

TTK preliminary results The TTK team is currently expanding the molecular library of redox active compounds beyond the pyridoxal database. The main aim is to build a structurally diverse set of molecules using the developed computational protocol, and apply machine learning techniques for high-throughput screening of the target 100 000 molecules. The TTK team is […]<\/p>\n","protected":false},"author":6,"featured_media":877,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[12],"tags":[],"_links":{"self":[{"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/posts\/873"}],"collection":[{"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/comments?post=873"}],"version-history":[{"count":7,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/posts\/873\/revisions"}],"predecessor-version":[{"id":900,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/posts\/873\/revisions\/900"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/media\/877"}],"wp:attachment":[{"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/media?parent=873"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/categories?post=873"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/tags?post=873"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}