Suppressing AI Deception in In Silico Drug Design and Molecular Docking

Objective & Hypothesis:In the field of de novo in silico biochemical / theoretical chemical drug design, this artificial intelligence (AI) software developing research paper, a new programming technique was discovered to teach AI not to bring up false data.

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Key Findings & Data:The increasing use of AI systems creates a tendency to produce misleading, incomplete, or overly confident results, which has become a growing concern, known as AI deception.

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Conclusion & Impact:The AIs should play crucial roles in scientific investigation while staying entrusted.

🔬Design:Modeling & Simulation

📍Scope:Comprehensive Sample

⚙️Variables:moleküler kenetlenme · De Novo İlaç Tasarımı · Yapay Zekâ Aldatması

Abstract

In the field of de novo in silico biochemical / theoretical chemical drug design, this artificial intelligence (AI) software developing research paper, a new programming technique was discovered to teach AI not to bring up false data. The increasing use of AI systems creates a tendency to produce misleading, incomplete, or overly confident results, which has become a growing concern, known as AI deception. In academic content, such AI behavior has the potential to create many problems, since the false data might cause problematic scientific progress by the decision-makers. Thus, this research study focused on AI deceit from the perspectives of education and software development and describes a docking-oriented AI assistant to limit uncontrolled autonomy and enhance user convenience. The proposed architecture combines a language model with Bayesian probabilistic reasoning, rule-based filters, and tightly constrained automation scripts that interact with established molecular docking tools. Rather than allowing the AI to perform scientific scoring or make independent decisions, deterministic external tools handle all calculations. The AI is restricted to orchestration, uncertainty highlighting, and log interpretation. This design minimizes the chances for hallucinated outcomes, enhances transparency, and makes reproducibility easier. These results alter the AI's behavior, reducing its deceptive potential. The AIs should play crucial roles in scientific investigation while staying entrusted.

Özet

De novo ilaç tasarımında in silico biyokimya, teorik kimya dalında moleküler kenetlenmelerde güvenle kullanılabilmesi için bu yapay zekâ (YZ) yazılım geliştirme araştırma makalesinde, YZ'nin yanlış veri üretmemesini sağlamak için yeni bir programlama tekniği keşfedilmiştir. Yapay zekâ sistemlerinin giderek artan kullanımı, yanıltıcı, eksik veya aşırı güvenle sunulan sonuçlar üretme eğilimini ortaya çıkarmaktadır. Bu durum giderek büyüyen bir endişe kaynağı hâline gelmiş ve yapay zekâ aldatması olarak adlandırılmaktadır. Akademik içerikte bu tür yapay zekâ davranışı birçok soruna yol açma potansiyeline sahiptir; çünkü yanlış veriler karar vericiler tarafından kullanıldığında bilimsel ilerlemenin problemli bir şekilde yönlendirilmesine neden olabilir. Bu nedenle bu araştırma çalışması, yapay zekâ aldatmasını eğitim ve yazılım geliştirme perspektiflerinden ele almakta ve kontrolsüz özerkliği sınırlamayı ve kullanıcı kolaylığını artırmayı amaçlayan moleküler kenetlenme odaklı bir yapay zekâ asistanını tanımlamaktadır. Önerilen kodlama mimarisi; bir dil modelini Bayesçi olasılıksal akıl yürütme, kural tabanlı filtreler ve yerleşik moleküler kenetlenme araçlarıyla etkileşim kuran sıkı şekilde sınırlandırılmış otomasyon betikleri ile birleştirmektedir. Yapay zekânın bilimsel puanlama yapmasına veya bağımsız kararlar almasına izin vermek yerine, tüm hesaplamalar deterministik dış araçlar tarafından gerçekleştirilmektedir. Yapay zekâ yalnızca süreci koordine etmek, belirsizlikleri vurgulamak ve kayıtları (logları) yorumlamak ile sınırlandırılmıştır. Bu tasarım, hayal ürünü (halüsinasyon) sonuçların ortaya çıkma olasılığını en aza indirir, şeffaflığı artırır ve sonuçların yeniden üretilebilirliğini kolaylaştırır. Elde edilen sonuçlar yapay zekânın davranışını değiştirerek aldatıcı potansiyelini azaltmaktadır. Yapay zekâ sistemleri bilimsel araştırmalarda önemli roller üstlenmeli, ancak bunu yaparken güvenilirliklerini koruyarak hareket etmelidir.

Keywords

moleküler kenetlenme De Novo İlaç Tasarımı Yapay Zekâ Aldatması Bayesçi Olasılıksal Akıl Yürütme Güvenilir Yapay Zekâ molecular docking De Novo Drug Design AI Deception Bayesian Probabilistic Reasoning Trustworthy AI

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Cite This Article

Gökoluk, E., Tokay, I., Akketeci, E., Şimşek, D., Akhavan, E., Agar, S., Elmas, M. (2026). Suppressing AI Deception in In Silico Drug Design and Molecular Docking. Journal of Kocaeli Health and Technology University, Cilt: 4 Sayı: 1, 87–102. https://doi.org/10.66163/jokohtu.1911754

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