Publications & Presentations
Presentations
- Reverse Screen: Given One Molecule, Which Proteins Does It Bind? 40 Hours of Docking to 108 Seconds A walk through reverse screening and the retrieval step in front of docking, with reversescreen.ai.
- Two Comparisons May Be All We Need: A Foundation Model for Drug Discovery That Ranks, Not Predicts How the Family Foundation Model ranks compounds and targets rather than predicting a value, with familyfoundationmodel.com.
- My 38-Year Journey with Neural Networks: Can They Further Unlock Pichia's Potential? (Pichia 2024) PDF KEY PPT Presentation
- Building a business with and without scientific computing: The five W's and one H (San Diego ACS, 2016) KEY PPT M4V
- New strategies to engage more of the world with scientific app development and content deployment (San Francisco ACS, 2014) PPT
- Standing on the Shoulders of Giants: New strategies to involve more of the world with data mining and intelligence extraction (Nice, France - International 2014 II-SDV conference, 2014) PPT
- Expanding the Reach of ChemInformatics through Mobile Computing (New Orleans ACS, 2013)
- Integrating proprietary and public-domain data from Kinase publications and patents from a biopharmaceutical company's perspective. (Webinar, Sept 25, 2012)
- Why Future Medicines Will Need to be Discovered in the Cloud (San Francisco Bio-IT World Cloud Summit, 2012)
- Having a mobile app presence - necessary or nice to have (San Diego ACS, 2012)
- Using Structural and Receptor-Site Similarity to Generate New Matter Ideas (Denver ACS, 2011)
- Riding the Mobile Wave (Denver ACS, 2011)
- Catching the Mobile Wave WEB
- The Mobile Kinome (ChemAxon UGM, Budapest-5/20/10) 2010 Presentation
- Using knowledgebases of structure-activity-data, receptor-site and protein structural similarity to generate new matter ideas (ACS-D.C.-8/16/09)
- Using Sequence-, Structure- and Receptor-site Similarity to Generate New Matter Ideas within the Kinome (Accelrys Life Science Forums-6/09)
- Using Receptor-Site and Protein Structure Similarity to Generate New Matter Ideas (UK QSAR-5/14/09)
- Using Receptor-Site Similarity to LigandCross into New Diversity (CHI Fragment-Based Techniques-4/8/09)
- Exercising receptor-site similarity: From Off-Target Identification to Scaffold Hopping (ACS-8/17/08)
- Surveying ligand- and target-based similarities within the Kinome (ACS-8/17/08)
- Interrogating the Druggable Proteome: Target-Fishing and Drug Design (Beijing-10/23/08)
Papers
- Human Intestinal Absorption Revisited: The 1998 Descriptors Survive, and the Method Extends to Permeability Muskal, S. M. (ORCID); Wessel, M. D. bioRxiv 2026 (DOI: 10.64898/2026.10.03.756481). CC BY 4.0. The method behind predicthia.ai and cHIA.
- Toxicity by Consortium Revisited: Structural Neighbors and Cross-Family Reach Muskal, S. M. (ORCID); Jha, S. K.; Kishore, P.; Tyagi, P. bioRxiv 2026 (DOI: 10.64898/2026.10.04.756566). CC BY 4.0. The method behind toxpred.ai.
- Two Comparators May Be All We Need Muskal, S. M. (ORCID). bioRxiv 2026 (DOI: 10.64898/2026.09.15.751854). CC BY 4.0. The method behind the Family Foundation Model.
- ReverseScreen.ai: Pharmacophore-Guided Reverse Screening Across the Growing Co-Complex Proteome Muskal, S. M. (ORCID); Nicola, G. bioRxiv 2026 (DOI: 10.64898/2026.09.09.750461). CC BY 4.0. The method behind reversescreen.ai.
- PharmCast: rapid generation of three-dimensional pharmacophore fingerprints from two-dimensional structure without conformer generation Muskal, S. M. (ORCID); McGregor, M. J. bioRxiv 2026 (DOI: 10.64898/2026.09.02.748999). CC BY 4.0. Describes PharmCast version 10.
- Sonic Stimulation and Low Power Microwave Radiation Can Modulate Bacterial Virulence Towards Caenorhabditis elegans Anti-Infective Agents, 2019, 17, 150-162 (DOI: 10.2174/2211352516666181102150049)
- High quality, small molecule-activity datasets for kinase research F1000Research, version 3, peer review: 2 approved (DOI: 10.12688/f1000research.8950.3) Earlier: version 2 PDF
- Lovastatin lactone may improve irritable bowel syndrome with constipation (IBS-C) by inhibiting enzymes in the archaeal methanogenesis pathway F1000Research. April 8, 2016 (DOI: 10.12688/f1000research.8406.3) Figure 4. Lovastatin-lactone v. F420 in the A5UMI1 site
- Kinome-wide Activity Modeling from Diverse Public High-Quality Data Sets J. Chem. Inf. Model. August 24, 2012 (DOI: 10.1021/ci300403k)
- Novel Kinase Inhibitors by Reshuffling Ligand Functionalities Across the Human Kinome J. Chem. Inf. Model. November 5, 2012 (DOI: 10.1021/ci3003842)
- Reaction101 and Yield101: Two mobile apps for chemistry with pedagogical value Whitepaper (2011)
- Interrogating the druggable genome with structural informatics MolecularDiversity (2006)
- StructSorter: A Method for Continuously Updating a Comprehensive Protein Structure Alignment Database J. Chem. Inf. Model. 2006, 46, 1871-1876
- STRUCTFAST: Protein Sequence Remote Homology Detection and Alignment Using Novel Dynamic Programming and Profile-Profile Scoring Proteins. 2006 64:960-967
- Convergent Island Statistics: A fast method for determining local alignment score significance. Bioinformatics, 2005, 21, 2827-2831.
- Prospective Exploration of Synthetically Feasible, Medicinally Relevant Chemical Space J.Chem. Inf. Model. 2005, 45, 239-248
- Software for Automating Analysis if Encoded Conbinatorial Libraries Combinatorial Chemistry & High Throughput Screening, 2002, 5, 531-543
- Pharmacophore Fingerprinting. 1. Application to QSAR and Focused Library Design J. Chem. Inf. Comput. Sci. 1999, 39, 569-574
- Pharmacophore Fingerprinting. 2. Application to Primary Library Design J Chem Inf. Comput. Sci. 2000, 40, 117-25
- A Simple and Readily Integratable Approach to Toxicity Prediction J. Chem. Inf. Comput. Sci. 2003, 43, 1673-1678
- Prediction of Human Intestinal Absorption of Drug Compounds from Molecular Structure J. Chem. Inf. Comput. Sci. 1998, 38, 726-735
- Artificial Intelligence and Molecular Biology: Chapter 4 Predicting Protein Structural Features With Artificial Neural Networks Cambridge, MIT Press Publ, 1993
- Predicting Protein Secondary Structure Content: A Tandem Neural Network Approach J. Mol. Biol. (1992) 225, 713-727
- Predicting surface exposure of amino acids from protein sequence Protein Engineering vol. 3 no. 8 pp. 659-665, 1990
- Prediction of the disulfide-bonding state of cysteine in proteins Protein Engineering vol. 3 no. 8 pp. 667-672, 1990
Conference Archive
Eidogen-Sertanty has attended the following events in the past.