Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis

• Published in: Life (Basel, Switzerland) Vol. 12; no. 3; p. 363
• Main Authors: Kenny, Sophie E, Antaw, Fiach, Locke, Warwick J, Howard, Christopher B, Korbie, Darren, Trau, Matt
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.03.2022; MDPI
• Subjects: Animals; Antibodies; antibody and peptide discovery; Antigens; Artificial intelligence; Big Data; Binding sites; Biological products; chemical libraries; Clinical trials; Coronaviruses; COVID-19; drug development; Drugs; high-throughput library screening; Immune system; Laboratories; Libraries; Machine learning; Medical research; Molecular interactions; Molecular structure; Pandemics; Patients; Protein engineering; Protein structure; Proteins; Review; Severe acute respiratory syndrome coronavirus 2; therapeutics; Venom; Viruses; Australia; therapeutics; high-throughput library screening; phage display; chemical libraries; drug development; antibody and peptide discovery; artificial intelligence; protein folding prediction
• ISSN: 2075-1729; 2075-1729
• DOI: 10.3390/life12030363

Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered "bottom-up" approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of "big data" and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure-when accompanied by high-quality experimental data for model training-has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this.